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@article{BomParaComparacaoRojas2016,
	title = {Process mining in healthcare: A literature review},
	journal = {Journal of Biomedical Informatics},
	volume = {61},
	pages = {224-236},
	year = {2016},
	issn = {1532-0464},
	doi = {https://doi.org/10.1016/j.jbi.2016.04.007},
	url = {https://www.sciencedirect.com/science/article/pii/S1532046416300296},
	author = {Eric Rojas and Jorge Munoz-Gama and Marcos Sepúlveda and Daniel Capurro},
	keywords = {Healthcare, Processes, Process mining, Case studies, Literature review},
	abstract = {Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.}
}

@Article{Lahmiri2019,
	author    = {Lahmiri, Salim and Shmuel, Amir},
	journal   = {Biomedical signal processing and control},
	title     = {Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine},
	year      = {2019},
	issn      = {1746-8094},
	pages     = {427--433},
	volume    = {49},
	abstract  = {Parkinson’s disease (PD) is a neurodegenerative disorder that causes severe motor and cognitive dysfunctions. Several types of physiological signals can be analyzed to accurately detect PD by using machine learning methods. This work considers the diagnosis of PD based on voice patterns. In particular, we focus on assessing the performance of eight different pattern ranking techniques (also termed feature selection methods) when coupled with nonlinear support vector machine (SVM) to distinguish between PD patients and healthy control subjects. The parameters of the radial basis function kernel of the SVM classifier were optimized by using Bayesian optimization technique. Our results show that the receiver operating characteristic and the Wilcoxon-based ranking techniques provide the highest sensitivity and specificity.},
	copyright = {2018 Elsevier Ltd},
	keywords  = {Bayesian optimization ; Classification ; Features ranking ; Parkinson’s disease ; Radial basis function ; Support vector machine ; Voice disorder},
	language  = {eng},
	publisher = {Elsevier Ltd},
}

@Article{Suppa2022,
	author   = {Suppa, Antonio and Costantini, Giovanni and Asci, Francesco and Di Leo, Pietro and Al-Wardat, Mohammad Sami and Di Lazzaro, Giulia and Scalise, Simona and Pisani, Antonio and Saggio, Giovanni},
	journal  = {Frontiers in Neurology},
	title    = {Voice in Parkinson's Disease: A Machine Learning Study},
	year     = {2022},
	issn     = {1664-2295},
	volume   = {13},
	abstract = {<sec>IntroductionParkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy.</sec><sec>MethodsWe investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations.</sec><sec>ResultsVoice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning.</sec><sec>ConclusionVoice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.</sec>},
	doi      = {10.3389/fneur.2022.831428},
	url      = {https://www.frontiersin.org/articles/10.3389/fneur.2022.831428},
}

@Article{Almeida2019,
	author   = {Jefferson S. Almeida and Pedro P. {Rebouças Filho} and Tiago Carneiro and Wei Wei and Robertas Damaševičius and Rytis Maskeliūnas and Victor Hugo C. {de Albuquerque}},
	journal  = {Pattern Recognition Letters},
	title    = {Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques},
	year     = {2019},
	issn     = {0167-8655},
	pages    = {55-62},
	volume   = {125},
	abstract = {This study investigates the processing of voice signals for detecting Parkinson’s disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.},
	doi      = {https://doi.org/10.1016/j.patrec.2019.04.005},
	keywords = {Parkinson’s disease, Speech processing, Phonological features, Feature extraction, Machine learning, Diagnosis},
	url      = {https://www.sciencedirect.com/science/article/pii/S0167865519301163},
}

@Article{SantosViana2021,
	author  = {dos Santos Viana, Luciana and Fantin, Cleiton},
	journal = {Journal of Pharmacy and Pharmacology},
	title   = {Evolution of Mortality Due to Parkinson’s Disease in the Capitals of the Brazilian States in the Period from 2010 to 2019},
	year    = {2021},
	pages   = {263--272},
	volume  = {9},
}

@Article{Tillmann2020,
	author    = {Tillmann, Ana Cristina and Swarowsky, Alessandra and Andrade, Alexandro and Moratelli, J{\'e}ssica and Boing, Leonessa and Vieira, Melissa de Carvalho Souza and Leit{\~a}o, Alice Erwig and Guimar{\~a}es, Adriana Coutinho de Azevedo},
	journal   = {Revista Brasileira de Medicina do Esporte},
	title     = {The impact of Brazilian samba on Parkinson’s disease: Analysis by the disease subtypes},
	year      = {2020},
	pages     = {25--29},
	volume    = {26},
	publisher = {SciELO Brasil},
}

@Article{SantosLobato2021,
	author    = {Santos-Lobato, Bruno L and Schumacher-Schuh, Artur and Mata, Ignacio F and Letro, Grace H and Braga-Neto, Pedro and Brand{\~a}o, Pedro RP and Godeiro-Junior, Cl{\'e}cio O and Coletta, Marcus V Della and Camargos, Sarah T and Borges, Vanderci and others},
	journal   = {Arquivos de Neuro-Psiquiatria},
	title     = {Genetics of Parkinson’s disease in Brazil: a systematic review of monogenic forms},
	year      = {2021},
	pages     = {612--623},
	volume    = {79},
	publisher = {SciELO Brasil},
}

@Article{Andreis2022,
	author    = {Andreis, Lucia M and Mariano, Marilia and Silva, Lorenna WL and Bianco, Claudia D and Rosa Neto, Francisco},
	journal   = {Perceptual and Motor Skills},
	title     = {The Impact of Parkinson’s Disease on General and Specific Motor Aptitudes: A Study of Older Brazilian Adults},
	year      = {2022},
	number    = {2},
	pages     = {253--268},
	volume    = {129},
	publisher = {SAGE Publications Sage CA: Los Angeles, CA},
}

@Article{Nassif2022,
	author    = {Nassif, Daniel Venturino and Pereira, Jo{\~a}o Santos},
	journal   = {Dementia \& Neuropsychologia},
	title     = {Fatigue in Brazilian patients with Parkinson’s disease},
	year      = {2022},
	pages     = {237--242},
	volume    = {16},
	publisher = {SciELO Brasil},
}

@Article{Rieder2020,
	author    = {Rieder, Carlos R},
	journal   = {Brazilian Journal of Psychiatry},
	title     = {Cannabidiol in Parkinson’s disease},
	year      = {2020},
	pages     = {126--127},
	volume    = {42},
	publisher = {SciELO Brasil},
}

@Article{Saba2022,
	author    = {Saba, Roberta Arb and Maia, D{\'e}bora Palma and Cardoso, Francisco Eduardo Costa and Borges, Vanderci and F Andrade, Luiz Augusto and Ferraz, Henrique Ballalai and Barbosa, Egberto Reis and Rieder, Carlos Roberto de Mello and da Silva, Delson Jos{\'e} and Chien, Hsin Fen and others},
	journal   = {Arquivos de Neuro-Psiquiatria},
	title     = {Guidelines for Parkinson’s disease treatment: consensus from the Movement Disorders Scientific Department of the Brazilian Academy of Neurology-motor symptoms},
	year      = {2022},
	pages     = {316--329},
	volume    = {80},
	publisher = {SciELO Brasil},
}

@Article{Tillmann2020a,
	author    = {Tillmann, Ana Cristina and Swarowsky, Alessandra and Corr{\^e}a, Clynton Louren{\c{c}}o and Andrade, Alexandro and Moratelli, J{\'e}ssica and Boing, Leonessa and Vieira, Melissa de Carvalho Souza and Araujo, Camila da Cruz Ramos de and Guimar{\~a}es, Adriana Coutinho de Azevedo},
	journal   = {Arquivos de Neuro-Psiquiatria},
	title     = {Feasibility of a Brazilian samba protocol for patients with Parkinson's disease: a clinical non-randomized study},
	year      = {2020},
	pages     = {13--20},
	volume    = {78},
	publisher = {SciELO Brasil},
}

@Article{Tracy2020,
	author    = {Tracy, John M and {\"O}zkanca, Yasin and Atkins, David C and Ghomi, Reza Hosseini},
	journal   = {Journal of Biomedical Informatics},
	title     = {Investigating voice as a biomarker: deep phenotyping methods for early detection of Parkinson's disease},
	year      = {2020},
	pages     = {103362},
	volume    = {104},
	publisher = {Elsevier},
}

@Article{Tougui2022,
	author    = {Tougui, Ilias and Jilbab, Abdelilah and El Mhamdi, Jamal},
	journal   = {Healthcare Informatics Research},
	title     = {Machine Learning Smart System for Parkinson Disease Classification Using the Voice as a Biomarker},
	year      = {2022},
	number    = {3},
	pages     = {210--221},
	volume    = {28},
	publisher = {Korean Society of Medical Informatics},
}

@Article{Ozkanca2019,
	author    = {Ozkanca, Yasin and {\"O}zt{\"u}rk, Mira{\c{c}} G{\"o}ksu and Ekmekci, Merve Nur and Atkins, David C and Demiroglu, Cenk and Ghomi, Reza Hosseini},
	journal   = {Digital biomarkers},
	title     = {Depression screening from voice samples of patients affected by parkinson’s disease},
	year      = {2019},
	number    = {2},
	pages     = {72--82},
	volume    = {3},
	publisher = {Karger Publishers},
}

@Article{Mei2021,
	author    = {Mei, Jie and Desrosiers, Christian and Frasnelli, Johannes},
	journal   = {Frontiers in aging neuroscience},
	title     = {Machine learning for the diagnosis of Parkinson's disease: a review of literature},
	year      = {2021},
	pages     = {633752},
	volume    = {13},
	publisher = {Frontiers Media SA},
}

@Article{Wang2020,
	author    = {Wang, Wu and Lee, Junho and Harrou, Fouzi and Sun, Ying},
	journal   = {IEEE Access},
	title     = {Early detection of Parkinson’s disease using deep learning and machine learning},
	year      = {2020},
	pages     = {147635--147646},
	volume    = {8},
	publisher = {IEEE},
}

@Article{Arora2019,
	author  = {Arora, Shubham and Baghai-Ravary, Ladan},
	journal = {arXiv preprint arXiv:1905.10198},
	title   = {Voice-based detection of Parkinson's disease using convolutional neural networks},
	year    = {2019},
}

@Article{Gunduz2019,
	author    = {Gunduz, Hakan},
	journal   = {IEEE Access},
	title     = {Deep learning-based Parkinson’s disease classification using vocal feature sets},
	year      = {2019},
	pages     = {115540--115551},
	volume    = {7},
	publisher = {IEEE},
}

@Article{Quan2021,
	author    = {Quan, Changqin and Ren, Kang and Luo, Zhiwei},
	journal   = {IEEE Access},
	title     = {A deep learning based method for Parkinson’s disease detection using dynamic features of speech},
	year      = {2021},
	pages     = {10239--10252},
	volume    = {9},
	publisher = {IEEE},
}

@InProceedings{Mathur2019,
	author       = {Mathur, Richa and Pathak, Vibhakar and Bandil, Devesh},
	booktitle    = {Emerging Trends in Expert Applications and Security: Proceedings of ICETEAS 2018},
	title        = {Parkinson disease prediction using machine learning algorithm},
	year         = {2019},
	organization = {Springer},
	pages        = {357--363},
}

@Article{Tsanas2020,
	author    = {Tsanas, Athanasios and Fox, Chris and Little, Max A and Ramig, Lorraine O},
	journal   = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
	title     = {A novel telemonitoring system for the assessment of voice and speech quality in Parkinson's disease},
	year      = {2020},
	number    = {2},
	pages     = {370--380},
	volume    = {28},
	publisher = {IEEE},
}

@Article{Khan2021,
	author    = {Khan, Nabeel and Singh, Vipul and Aldosari, Faisal and Karuppiah, Ramkumar and Dhami, Dinesh Singh},
	journal   = {Neurological Sciences},
	title     = {Deep learning in the detection of Parkinson's disease: a systematic review},
	year      = {2021},
	publisher = {Springer},
}

@Article{Gaur2023,
	author    = {Gaur, Savita and Kalani, Priti and Mohan, M},
	journal   = {Medical Journal Armed Forces India},
	title     = {Harmonic-to-noise ratio as speech biomarker for fatigue: K-nearest neighbour machine learning algorithm},
	year      = {2023},
	publisher = {Elsevier},
}

@Article{Amato2023,
	author    = {Amato, Federica and Saggio, Giovanni and Cesarini, Valerio and Olmo, Gabriella and Costantini, Giovanni},
	journal   = {Expert Systems with Applications},
	title     = {Machine Learning-and Statistical-based Voice Analysis of Parkinson’s Disease Patients: A Survey},
	year      = {2023},
	pages     = {119651},
	publisher = {Elsevier},
}

@Article{Byeon2021,
	author    = {Byeon, Haewon},
	journal   = {Applied Sciences},
	title     = {Comparing ensemble-based machine learning classifiers developed for distinguishing hypokinetic dysarthria from presbyphonia},
	year      = {2021},
	number    = {5},
	pages     = {2235},
	volume    = {11},
	publisher = {MDPI},
}

@Article{Redd2019,
	author    = {Redd, Travis K and Campbell, John Peter and Brown, James M and Kim, Sang Jin and Ostmo, Susan and Chan, Robison Vernon Paul and Dy, Jennifer and Erdogmus, Deniz and Ioannidis, Stratis and Kalpathy-Cramer, Jayashree and others},
	journal   = {British Journal of Ophthalmology},
	title     = {Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity},
	year      = {2019},
	number    = {5},
	pages     = {580--584},
	volume    = {103},
	publisher = {BMJ Publishing Group Ltd},
}

@Article{Loh2021,
	author    = {Loh, Hui Wen and Hong, Wanrong and Ooi, Chui Ping and Chakraborty, Subrata and Barua, Prabal Datta and Deo, Ravinesh C and Soar, Jeffrey and Palmer, Elizabeth E and Acharya, U Rajendra},
	journal   = {Sensors},
	title     = {Application of deep learning models for automated identification of Parkinson’s disease: a review (2011--2021)},
	year      = {2021},
	number    = {21},
	pages     = {7034},
	volume    = {21},
	publisher = {Multidisciplinary Digital Publishing Institute},
}

@Article{Pahuja2022,
	author    = {Pahuja, Gunjan and Prasad, Bhanu},
	journal   = {Computers in Biology and Medicine},
	title     = {Deep learning architectures for Parkinson's disease detection by using multi-modal features},
	year      = {2022},
	pages     = {105610},
	volume    = {146},
	publisher = {Elsevier},
}

@Article{Thakur2023,
	author    = {Thakur, Mahima and Dhanalakshmi, Samiappan and Kuresan, Harisudha and Senthil, Ramalingam and Narayanamoorthi, R and Lai, Khin Wee},
	journal   = {Journal of Ambient Intelligence and Humanized Computing},
	title     = {Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson’s disease using digitized spiral drawings},
	year      = {2023},
	number    = {1},
	pages     = {175--189},
	volume    = {14},
	publisher = {Springer},
}

@Article{Haq2022,
	author    = {ul Haq, Amin and Li, Jian Ping and Agbley, Bless Lord Y and Mawuli, Cobbinah Bernard and Ali, Zafar and Nazir, Shah and Din, Salah Ud},
	journal   = {Expert Systems with Applications},
	title     = {A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data},
	year      = {2022},
	pages     = {118045},
	volume    = {208},
	publisher = {Elsevier},
}

@Article{Shafiq2022,
	author    = {Shafiq, Shakila and Ahmed, Sabbir and Kaiser, M Shamim and Mahmud, Mufti and Hossain, Md Shahadat and Andersson, Karl},
	journal   = {IEEE Access},
	title     = {Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis},
	year      = {2022},
	publisher = {IEEE},
}

@Article{Shibata2022,
	author    = {Shibata, Haruto and Uchida, Yuto and Inui, Shohei and Kan, Hirohito and Sakurai, Keita and Oishi, Naoya and Ueki, Yoshino and Oishi, Kenichi and Matsukawa, Noriyuki},
	journal   = {Parkinsonism \& Related Disorders},
	title     = {Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease},
	year      = {2022},
	pages     = {104--110},
	volume    = {94},
	publisher = {Elsevier},
}

@article{dos2021evolution,
	title={Evolution of Mortality Due to Parkinson’s Disease in the Capitals of the Brazilian States in the Period from 2010 to 2019},
	author={dos Santos Viana, Luciana and Fantin, Cleiton},
	journal={Journal of Pharmacy and Pharmacology},
	volume={9},
	pages={263--272},
	year={2021}
}

@article{tillmann2020impact,
	title={The impact of Brazilian samba on Parkinson’s disease: Analysis by the disease subtypes},
	author={Tillmann, Ana Cristina and Swarowsky, Alessandra and Andrade, Alexandro and Moratelli, J{\'e}ssica and Boing, Leonessa and Vieira, Melissa de Carvalho Souza and Leit{\~a}o, Alice Erwig and Guimar{\~a}es, Adriana Coutinho de Azevedo},
	journal={Revista Brasileira de Medicina do Esporte},
	volume={26},
	pages={25--29},
	year={2020},
	publisher={SciELO Brasil}
}

@article{santos2021genetics,
	title={Genetics of Parkinson’s disease in Brazil: a systematic review of monogenic forms},
	author={Santos-Lobato, Bruno L and Schumacher-Schuh, Artur and Mata, Ignacio F and Letro, Grace H and Braga-Neto, Pedro and Brand{\~a}o, Pedro RP and Godeiro-Junior, Cl{\'e}cio O and Della Coletta, Marcus V and Camargos, Sarah T and Borges, Vanderci and others},
	journal={Arquivos de Neuro-Psiquiatria},
	volume={79},
	number={07},
	pages={612--623},
	year={2021},
	publisher={Thieme Revinter Publica{\c{c}}{\~o}es Ltda.}
}

@article{andreis2022impact,
	title={The Impact of Parkinson’s Disease on General and Specific Motor Aptitudes: A Study of Older Brazilian Adults},
	author={Andreis, Lucia M and Mariano, Marilia and Silva, Lorenna WL and Bianco, Claudia D and Rosa Neto, Francisco},
	journal={Perceptual and Motor Skills},
	volume={129},
	number={2},
	pages={253--268},
	year={2022},
	publisher={SAGE Publications Sage CA: Los Angeles, CA}
}


@article{rieder2020cannabidiol,
	title={Cannabidiol in Parkinson’s disease},
	author={Rieder, Carlos R},
	journal={Brazilian Journal of Psychiatry},
	volume={42},
	pages={126--127},
	year={2020},
	publisher={SciELO Brasil}
}


@article{mei2021machine,
	title={Machine learning for the diagnosis of Parkinson's disease: a review of literature},
	author={Mei, Jie and Desrosiers, Christian and Frasnelli, Johannes},
	journal={Frontiers in aging neuroscience},
	volume={13},
	pages={633752},
	year={2021},
	publisher={Frontiers Media SA}
}


@article{tillmann2020feasibility,
	title={Feasibility of a Brazilian samba protocol for patients with Parkinson's disease: a clinical non-randomized study},
	author={Tillmann, Ana Cristina and Swarowsky, Alessandra and Corr{\^e}a, Clynton Louren{\c{c}}o and Andrade, Alexandro and Moratelli, J{\'e}ssica and Boing, Leonessa and Vieira, Melissa de Carvalho Souza and de ARAUJO, Camila da Cruz Ramos and de Azevedo Guimar{\~a}es, Adriana Coutinho},
	journal={Arquivos de neuro-psiquiatria},
	volume={78},
	number={01},
	pages={13--20},
	year={2020},
	publisher={Thieme Revinter Publica{\c{c}}{\~o}es Ltda.}
}

@article{saba2022guidelines,
	title={Guidelines for Parkinson’s disease treatment: consensus from the Movement Disorders Scientific Department of the Brazilian Academy of Neurology-motor symptoms},
	author={Saba, Roberta Arb and Maia, D{\'e}bora Palma and Cardoso, Francisco Eduardo Costa and Borges, Vanderci and F Andrade, Luiz Augusto and Ferraz, Henrique Ballalai and Barbosa, Egberto Reis and Rieder, Carlos Roberto de Mello and da Silva, Delson Jos{\'e} and Chien, Hsin Fen and others},
	journal={Arquivos de Neuro-Psiquiatria},
	volume={80},
	pages={316--329},
	year={2022},
	publisher={SciELO Brasil}
}

@article{ozkanca2019depression,
	title={Depression screening from voice samples of patients affected by parkinson’s disease},
	author={Ozkanca, Yasin and G{\"o}ksu {\"O}zt{\"u}rk, Mira{\c{c}} and Ekmekci, Merve Nur and Atkins, David C and Demiroglu, Cenk and Hosseini Ghomi, Reza},
	journal={Digital biomarkers},
	volume={3},
	number={2},
	pages={72--82},
	year={2019},
	publisher={S. Karger AG}
}

@article{tougui2022machine,
	title={Machine learning smart system for Parkinson disease classification using the voice as a biomarker},
	author={Tougui, Ilias and Jilbab, Abdelilah and El Mhamdi, Jamal},
	journal={Healthcare Informatics Research},
	volume={28},
	number={3},
	pages={210--221},
	year={2022},
	publisher={Korean Society of Medical Informatics}
}

@article{dias2003treatment,
	title={Treatment of vocal symptoms in Parkinson's disease: the Lee Silverman method},
	author={Dias, Alice Estevo and Limongi, Jo{\~a}o Carlos Papaterra},
	journal={Arquivos de neuro-psiquiatria},
	volume={61},
	pages={61--66},
	year={2003},
	publisher={SciELO Brasil}
}



@article{nassif2022fatigue,
	title={Fatigue in Brazilian patients with Parkinson’s disease},
	author={Nassif, Daniel Venturino and Pereira, Jo{\~a}o Santos},
	journal={Dementia \& Neuropsychologia},
	volume={16},
	pages={237--242},
	year={2022},
	publisher={SciELO Brasil}
}



@article{ahmed2022,
	title={Classification of Parkinson disease based on patient’s voice signal using machine learning},
	author={Ahmed, Imran and Aljahdali, Sultan and Khan, Muhammad Shakeel and Kaddoura, Sanaa},
	journal={Intelligent Automation and Soft Computing},
	volume={32},
	number={2},
	pages={705},
	year={2022},
	publisher={Computers, Materials and Continua (Tech Science Press)}
}

@article{rana2022,
	title={An Efficient Machine Learning Approach for Diagnosing Parkinson’s Disease by Utilizing Voice Features},
	author={Rana, Arti and Dumka, Ankur and Singh, Rajesh and Rashid, Mamoon and Ahmad, Nazir and Panda, Manoj Kumar},
	journal={Electronics},
	volume={11},
	number={22},
	pages={3782},
	year={2022},
	publisher={MDPI}
}

@article{ouhmida2021,
	title={A Novel Approach for Parkinson’s Disease Detection Based on Voice Classification and Features Selection Techniques},
	author={Ouhmida, Asmae and Raihani, Abdelhadi and Cherradi, Bouchaib and Terrada, Oumaima},
	journal={Int. J. Online Eng},
	volume={17},
	pages={111},
	year={2021}
}

@Article{bios22,
	AUTHOR = {Bao, Guidong and Lin, Mengchen and Sang, Xiaoqian and Hou, Yangcan and Liu, Yixuan and Wu, Yunfeng},
	TITLE = {Classification of Dysphonic Voices in Parkinsons Disease with Semi-Supervised Competitive Learning Algorithm},
	JOURNAL = {Biosensors},
	VOLUME = {12},
	YEAR = {2022},
	NUMBER = {7},
	ARTICLE-NUMBER = {502},
	URL = {https://www.mdpi.com/2079-6374/12/7/502},
	PubMedID = {35884305},
	ISSN = {2079-6374},
	ABSTRACT = {This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson&rsquo;s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson&rsquo;s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann&ndash;Whitney&ndash;Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p &lt; 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.},
	DOI = {10.3390/bios12070502}
}

@INPROCEEDINGS{Polat2019,
	author={Polat, Kemal},
	booktitle={2019 Scientific Meeting on Electrical-Electronics \& Biomedical Engineering and Computer Science (EBBT)}, 
	title={A Hybrid Approach to Parkinson Disease Classification Using Speech Signal: The Combination of SMOTE and Random Forests}, 
	year={2019},
	pages={1-3},
	doi={10.1109/EBBT.2019.8741725}}

@InProceedings{Nahar-etal2021,
	author={Nahar, Nazmun and Ara, Ferdous and Neloy, Md. Arif Istiek and Biswas, Anik and Hossain, Mohammad Shahadat and Andersson, Karl},
	editor={Mahmud, Mufti and Kaiser, M. Shamim and Vassanelli, Stefano and Dai, Qionghai and Zhong, Ning},
	title={Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease},
	booktitle={Brain Informatics},
	year={2021},
	publisher={Springer International Publishing},
	address={Cham},
	pages={496--508},
	abstract={Parkinson's disease (PD) is a kind of neurodegenerative disorder characterized by the loss of dopamine-producing cells in the brain. The disruption of brain cells that create dopamine, a chemical that allows brain cells to connect with one another, causes Parkinson's disease. Control, adaptability, and rapidity of movement are all controlled by dopamine-producing cells in the brain. Researchers have been investigating for techniques to identify non-motor symptoms that show early in the disease as soon as possible, slowing the disease's progression. A machine learning-based detection of Parkinson's disease is proposed in this research. Feature selection and classification techniques are used in the proposed detection technique. Boruta, Recursive Feature Elimination (RFE) and Random Forest (RF) Classifier have been used for the feature selection process. Four classification algorithms are considered to detect Parkinson disease which are gradient boosting, extreme gradient boosting, bagging and Extra Tree Classifier. Bagging with recursive feature elimination was found to outperform the other methods. The lowest number of voice characteristics for the diagnosis in Parkinson attained 82.35{\%} accuracy.},
	isbn={978-3-030-86993-9}}

@article{Solana-Lavalle2020,
	title = {Automatic Parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features},
	journal = {Biocybernetics and Biomedical Engineering},
	volume = {40},
	number = {1},
	pages = {505-516},
	year = {2020},
	issn = {0208-5216},
	doi = {https://doi.org/10.1016/j.bbe.2020.01.003},
	url = {https://www.sciencedirect.com/science/article/pii/S0208521620300085},
	author = {Gabriel Solana-Lavalle and Juan-Carlos Galán-Hernández and Roberto Rosas-Romero},
	keywords = {Parkinson disease, Dysphonia features, Feature subset selection, Support vector machine},
	abstract = {Recent research on Parkinson disease (PD) detection has shown that vocal disorders are linked to symptoms in 90\% of the PD patients at early stages. Thus, there is an interest in applying vocal features to the computer-assisted diagnosis and remote monitoring of patients with PD at early stages. The contribution of this research is an increase of accuracy and a reduction of the number of selected vocal features in PD detection while using the newest and largest public dataset available. Whereas the number of features in this public dataset is 754, the number of selected features for classification ranges from 8 to 20 after using Wrappers feature subset selection. Four classifiers (k nearest neighbor, multi-layer perceptron, support vector machine and random forest) are applied to vocal-based PD detection. The proposed approach shows an accuracy of 94.7\%, sensitivity of 98.4\%, specificity of 92.68\% and precision of 97.22\%. The best resulting accuracy is obtained by using a support vector machine and it is higher than the one, which was reported on the first work to use the same dataset. In addition, the corresponding computational complexity is further reduced by selecting no more than 20 features.}}

@article{Avuclu2020,
	title = {Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements},
	author = {Avuclu, Emre and Elen, Abdullah},
	journal={Medical \& Biological Engineering \& Computing},
	volume={58},
	pages={2775–2788},
	issn = {1741-0444},
	year={2020},
	publisher={Springer},
	doi = {10.1007/s11517-020-02260-3},
	url = {https://doi.org/10.1007/s11517-020-02260-3}}

@article{Lamba2022,
	title = {A Hybrid Feature Selection Approach for Parkinson’s Detection Based on Mutual Information Gain and Recursive Feature Elimination},
	author = {Lamba, R. and Gulati, T. and Jain, A.},
	journal={Arabian Journal for Science and Engineering},
	volume={47},
	pages={10263–10276},
	issn = {2191-4281},
	year={2022},
	publisher={Springer},
	doi = {10.1007/s13369-021-06544-0},
	url = {https://doi.org/10.1007/s13369-021-06544-0}}

@article{Yadav2021,
	title = {Hybrid Machine Learning Classifier and Ensemble Techniques to Detect Parkinson’s Disease Patients},
	author = {Yadav, Sunil and Singh, Munindra Kumar},
	journal={SN Computer Science},
	volume={2},
	issn = {2661-8907},
	year={2021},
	publisher={Springer},
	doi = {10.1007/s42979-021-00587-8},
	url = {https://doi.org/10.1007/s42979-021-00587-8}}

@article{yaman2020,
	title={Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features},
	author={Yaman, Orhan and Ertam, Fatih and Tuncer, Turker},
	journal={Medical hypotheses},
	volume={135},
	pages={109483},
	year={2020},
	publisher={Elsevier}
}

@inproceedings{hussain2022,
	title={Machine Learning Techniques for Voice-based Early Detection of Parkinson's Disease},
	author={Hussain, Audil and Sharma, Amit},
	booktitle={2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)},
	pages={1436--1439},
	year={2022},
	organization={IEEE}
}

@article{nissar2019,
	title={Voice-based detection of Parkinson’s disease through ensemble machine learning approach: A Performance study},
	author={Nissar, Iqra and Rizvi, Danish Raza and Masood, Sarfaraz and Mir, Aqib Nazir},
	journal={EAI Endorsed Transactions on Pervasive Health and Technology},
	volume={5},
	number={19},
	pages={e2--e2},
	year={2019}
}

@book{maimon2014,
	title={Data mining with decision trees: theory and applications},
	author={Maimon, Oded Z and Rokach, Lior},
	volume={81},
	year={2014},
	publisher={World scientific}
}

@book{wade2020,
	title={Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python},
	author={Wade, Corey and Glynn, Kevin},
	year={2020},
	publisher={Packt Publishing Ltd}
}

@article{kavzoglu2022,
	title={Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost)},
	author={Kavzoglu, Taskin and Teke, Alihan},
	journal={Arabian Journal for Science and Engineering},
	volume={47},
	number={6},
	pages={7367--7385},
	year={2022},
	publisher={Springer}
}

@article{kramer2013k,
	title={K-nearest neighbors},
	author={Kramer, Oliver and Kramer, Oliver},
	journal={Dimensionality reduction with unsupervised nearest neighbors},
	pages={13--23},
	year={2013},
	publisher={Springer}
}

@article{jiang2008,
	title={A novel Bayes model: Hidden naive Bayes},
	author={Jiang, Liangxiao and Zhang, Harry and Cai, Zhihua},
	journal={IEEE Transactions on knowledge and data engineering},
	volume={21},
	number={10},
	pages={1361--1371},
	year={2008},
	publisher={IEEE}
}

@book{ma2014support,
	title={Support vector machines applications},
	author={Ma, Yunqian and Guo, Guodong},
	volume={649},
	year={2014},
	publisher={Springer}
}

@book{bhattacharyya2020deep,
	title={Deep Learning: Research and Applications},
	author={Bhattacharyya, Siddhartha and Snasel, Vaclav and Hassanien, Aboul Ella and Saha, Satadal and Tripathy, BK},
	volume={7},
	year={2020},
	publisher={Walter de Gruyter GmbH \& Co KG}
}

@article{ahsan2021effect,
	title={Effect of data scaling methods on machine learning algorithms and model performance},
	author={Ahsan, Md Manjurul and Mahmud, MA Parvez and Saha, Pritom Kumar and Gupta, Kishor Datta and Siddique, Zahed},
	journal={Technologies},
	volume={9},
	number={3},
	pages={52},
	year={2021},
	publisher={MDPI}
}

@book{duda2006pattern,
	title={Pattern classification},
	author={Duda, Richard O and Hart, Peter E and others},
	year={2006},
	publisher={John Wiley \& Sons}
}

@incollection{SINGH202189,
	title = {Chapter 5 - Diagnosing of disease using machine learning},
	editor = {Krishna Kant Singh and Mohamed Elhoseny and Akansha Singh and Ahmed A. Elngar},
	booktitle = {Machine Learning and the Internet of Medical Things in Healthcare},
	publisher = {Academic Press},
	pages = {89-111},
	year = {2021},
	isbn = {978-0-12-821229-5},
	doi = {https://doi.org/10.1016/B978-0-12-821229-5.00003-3},
	url = {https://www.sciencedirect.com/science/article/pii/B9780128212295000033},
	author = {Pushpa Singh and Narendra Singh and Krishna Kant Singh and Akansha Singh},
	keywords = {Machine learning, diagnosis, SVM, KNN, decision tree, performance, accuracy},
	abstract = {The role of machine learning in the healthcare industry is inevitable due to its power to use in disease detection and management. Disease diagnosis using machine-learning techniques can enhance the quickness of decision-making, and it can reduce the rate of false positives. This chapter discusses recent developments in machine-learning algorithms that have made a substantial impact on the detection and diagnosis of several diseases. This chapter firstly introduces challenges in the conventional healthcare system and then describes various machine-learning algorithms like SVM, KNN, Naive Bayes, and Decision tree. The practical implementation of these algorithms is discussed with Python. These algorithms are used to diagnose various diseases like cancer, diabetes, epilepsy, heart attack, and other prominent diseases. The text offers a conceptual and mathematical background of accuracy, precision, recall, and F1 score metrics of a machine-learning algorithm in order to diagnose disease. Finally, the chapter discusses the impact of machine learning on the healthcare industry.}
}

@article{AshourAmiraS.2020ANFo,
	issn = {2169-3536},
	abstract = {Parkinson's disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a novel feature selection framework using two levels of the feature selection procedure for voice-loss detection in PD patients. At the first level selection, the principal component analysis (PCA) and the eigenvector centrality feature selection (ECFS) methods are initially calculated independently, and the selected features from each method are considered as a separated sublist, namely ECFS selected features sublist, and PCA selected features sublist, in the first set. Accordingly, the first set, which is the first level selection set, is generated from the union of these two sublists using the top-selected features from both methods. In the training phase, a second level selection, which forms the second set (which is a subset from the first set), is generated to calculate the proposed weight of each selection method. Since in the present work, the ECFS provided superior performance to the PCA in the first level selection, the ECFS is applied to the first set in order to find weight values based on the contribution/impact of the top-selected PCA- and ECFS- features in the second level. This weight is determined by finding a proposed ratio, which is multiplied directly by the selected ECFS features in the first level. The selected weighted ECFS features are then combined with the same PCA features to avoid ignoring any of the top-ranked features from the first level. This combination includes the final weighted-hybrid selected features that fed to a support vector machine (SVM) classifier to evaluate the proposed weighted hybrid selected features. Hence, in the test phase, the generated weight is used directly without any further need for the second level selection. Several comparative studies were conducted to evaluate the proposed feature selection performance for PD voice-loss detection. The experimental results established the superiority of the proposed procedure using cubic kernel-SVM with 94% accuracy for voice-loss detection in PD, while, with the same classifier, 88% accuracy was achieved without using the proposed selection method.},
	journal = {IEEE access},
	pages = {76193--76203},
	volume = {8},
	publisher = {IEEE},
	year = {2020},
	title = {A Novel Framework of Two Successive Feature Selection Levels Using Weight-Based Procedure for Voice-Loss Detection in Parkinson's Disease},
	copyright = {Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020},
	language = {eng},
	address = {Piscataway},
	author = {Ashour, Amira S. and Nour, Majid Kamal A. and Polat, Kemal and Guo, Yanhui and Alsaggaf, Wafaa and El-Attar, Amira},
	keywords = {Acoustic measurements ; Classifiers ; Comparative studies ; eigenvector centrality feature selection ; Eigenvectors ; Feature extraction ; Feature selection ; Mathematical analysis ; Mel frequency cepstral coefficient ; Parkinson's disease ; Principal component analysis ; Principal components analysis ; Signal processing ; Signs and symptoms ; Speech processing ; Support vector machines ; voice loss ; Voice recognition ; Weight},
}

@Article{Peng1994,
	author    = {Peng, C-K and Buldyrev, Sergey V and Havlin, Shlomo and Simons, Michael and Stanley, H Eugene and Goldberger, Ary L},
	journal   = {Physical review e},
	title     = {Mosaic organization of DNA nucleotides},
	year      = {1994},
	number    = {2},
	pages     = {1685},
	volume    = {49},
	publisher = {APS},
}

@Article{SantosVianaL2021,
	author  = {dos Santos Viana L, Fantin C.},
	journal = {Revista de de Farmácia e Farmacologia},
	title   = {Evolução da Mortalidade por Doença de ParkinsonNosso estudo mostrou uma estrutura híbrida de redes neurais que alcançou o maior(2021), cuja abordagem baseada em CNN alcançou uma precisão de 89,75%. Por outro lado,nosso estudo teve um desempenho semelhante ao de Singh (2020) e Hires (2022), quealcançaram99% Com base nesses resultados, podemos concluir que técnicas de aprendizado demáquina, particularmente redes neurais artificiais, têm potencial significativo para diagnosticarDP com base na análise de voz. No entanto, o desempenho do modelo depende fortemente do 431 adequadohiperparâmetros, é possível obter uma melhora no desempenho do modelo.A avaliação dos hiperparâmetros revelou que a escolha de uma função de ativação e umsolver apropriados podem melhorar significativamente o desempenho do modelo. Em nosso 420de Farmácia e Farmacologia. 2021;9:263–272.as métricas de desempenho. No entanto, esta análise tornou-se onerosa a este trabalho edecidimos parar por aqui. ficando com a ideia de que através do ajuste adequado do 406acurácia diagnóstica entre os classificadores testados, com desempenho excepcional emacurácia, precisão, recall e F1-score, tanto na validação quanto nos conjuntos de teste. Essadescoberta é especialmente relevante quando se considera a necessidade de melhorar e adaptaressas técnicas para lidar com possíveis dificuldades nos dados.nas Capitais dos Estados Brasileiros no Período de 2010 a 2019.},
	year    = {2021},
}

@Article{2.Tillmann2020,
	author  = {2. Tillmann AC, Swarowsky A, Andrade A, Moratelli J, Boing L, Vieira MdCS, et al.},
	journal = {Revista a Brasileira deMedicina do Esporte.},
	title   = {O impacto dosamba brasileiro na doença de Parkinson: análise pelos subtipos da doença.},
	year    = {2020},
}

@Article{SantosLobato2021a,
	author  = {Santos-Lobato BL, Schumacher-Schuh A, Mata IF, Letro GH, Braga-Neto P , Brandão PR},
	journal = {Arquivos de Neuro-Psiquiatria},
	title   = {. Genética da doença de Parkinson no Brasil: uma revisão sistemática das formasmonogênicas},
	year    = {2021},
}

@Article{Andreis2022a,
	author  = {Andreis LM, Mariano M, Silva LW, Bianco CD, Rosa Neto F},
	journal = {Revista Saber Científico},
	title   = {O Impacto da Doença de Parkinson nas Aptidões Motoras Gerais e Específicas: Um Estudo em Idosos Brasileiros.Habilidades Perceptivas e Motoras.},
	year    = {2022},
}

@Article{Rieder2020a,
	author  = {Carlos R. Rieder},
	journal = {Revista Brasileira de Psiquiatria},
	title   = {Rieder CR. Canabidiol na doença de Parkinson},
	year    = {2020},
}

@Article{NassifDV2022,
	author  = {Nassif DV, Pereira JS},
	journal = {Dementia & Neuropsychologia},
	title   = {Fadiga em pacientes brasileiros com doença de Parkinson.Demência e Neuropsicologia},
	year    = {2022},
}

@Article{Tillmann2020b,
	author  = {Tillmann AC, Swarowsky A, Corréia CL, Andrade A, Moratelli J, Boing L},
	journal = {. Arquivos de Neuro-Psiquiatria},
	title   = {Viabilidade de um protocolo de samba brasileiro para pacientes com doença de Parkinson: um estudoclínico não randomizado},
	year    = {2020},
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@Article{Saba2022a,
	author  = {Saba RA, Maia DP, Cardoso FEC, Borges V, F Andrade LA, Ferraz HB},
	journal = {. Arquivos de Neuro-Psiquiatria},
	title   = {Diretrizes para o tratamento da doença de Parkinson: consenso do Departamento Científico deDistúrbios do Movimento da Academia Brasileira de Neurologia-sintomas motores.},
	year    = {2022},
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@Article{Ozkanca2022,
	author  = {Ozkanca Y, Ozturk MG, Ekmekci MN, Atkins DC, Demiroglu C, Ghomi RH},
	journal = {Pesquisa em Informática em Saúde},
	title   = {Triagem de depressão a partir de amostras de voz de pacientes afetados pela doença deparkinson. Biomarcadores digitais.},
	year    = {2022},
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@Article{Tougui2022a,
	author  = {Tougui I, Jilbab A, El Mhamdi J.},
	journal = {Pesquisa em Informática em Saúde.},
	title   = {Sistema Inteligente de Aprendizado de Máquina para Classificação daDoença de Parkinson Usando a Voz como um Biomarcador.},
	year    = {2022},
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@Article{Wang2020a,
	author  = {Wang W, Lee J, Harrou F, Sun Y},
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	title   = {. Detecção precoce da doença de Parkinson usando aprendizadoprofundo e aprendizado de máquina},
	year    = {2020},
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@Article{Mei2021a,
	author  = {Mei J, Desrosiers C, Frasnelli J},
	journal = {Fronteiras na neurociência do envelhecimento},
	title   = {Aprendizado de máquina para o diagnóstico da doença de Parkinson:uma revisão da literatura},
	year    = {2021},
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@Article{AroraS2019,
	author  = {Arora S, Baghai-Ravary L.},
	journal = {pré-impressão arXiv arXiv},
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	year    = {2019},
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@Article{Gunduz2019a,
	author  = {Gunduz H},
	journal = {Acesso IEEE},
	title   = {Classificação da doença de Parkinson baseada em aprendizado profundo usandoconjuntos de recursos vocais},
	year    = {2019},
}

@Article{Tsanas2020a,
	author  = {Tsanas A, Fox C, Little MA, Ramig LO},
	journal = {Transações IEEE em Sistemas Neurais eEngenharia de Reabilitação},
	title   = {. Um novo sistema de telemonitoramento para a avaliação daqualidade da voz e da fala na doença de Parkinson},
	year    = {2020},
}

@Article{Quan2021a,
	author  = {Quan C, Ren K, Luo Z},
	journal = {Acesso IEEE},
	title   = {Um método baseado em aprendizado profundo para a detecção da doençade Parkinson usando características dinâmicas da fala},
	year    = {2021},
}

@Article{Mathur2019a,
	author  = {Mathur R, Pathak V, Bandil D},
	journal = {Tendências emergentes em aplicativos especializados e segurança},
	title   = {Previsão da doença de Parkinson usando máquina algoritmo de aprendizagem},
	year    = {2019},
}

@Article{Khan2021a,
	author  = {Khan N, Singh V, Aldosari F, Karuppiah R, Dhami DS},
	journal = {Ciências Neurológicas},
	title   = {Aprendizagem profunda na detecção dadoença de Parkinson: uma revisão sistemática},
	year    = {2021},
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@Article{Gaur2023a,
	author  = {Gaur S, Kalani P, Mohan M.},
	journal = {Revista Médica das ForçasArmadas da Índia.},
	title   = {Relação harmônico-ruído como biomarcador de fala para fadiga: Algoritmo de aprendizado de máquina K-vizinho mais próximo},
	year    = {2023},
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@Article{Byeon2021a,
	author  = {Byeon H},
	journal = {Ciências Aplicadas},
	title   = {Comparando classificadores de aprendizado de máquina baseados em conjuntodesenvolvidos para distinguir disartria hipocinética de presbifonia},
	year    = {2021},
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@Article{Amato2023a,
	author  = {Amato F, Saggio G, Cesarini V, Olmo G, Costantini G},
	journal = {Sistemas Especialistas com Aplicações},
	title   = {Aprendizado de máquina e análise de vozbaseada em estatísticas de pacientes com doença de Parkinson:: uma pesquisa.},
	year    = {2023},
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@Article{Redd2019a,
	author  = {Redd TK, Campbell JP, Brown JM, Kim SJ, Ostmo S, Chan RVP},
	journal = {Jornal britânico de oftalmologia},
	title   = {Avaliação de um sistema de avaliação de imagem de aprendizado profundo para detectarretinopatia grave da prematuridade.},
	year    = {2019},
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@Article{Loh2021a,
	author  = {Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC},
	journal = {Sensores. 2021;21(21)},
	title   = {Aplicação de modelos de aprendizado profundo para identificação automatizada da doença deParkinson: uma revisão (2011–2021)},
	year    = {2021},
}

@Article{PahujaG2022,
	author  = {Pahuja G, Prasad B.},
	journal = {Computadores em Biologia e Medicina},
	title   = {Arquiteturas de aprendizado profundo para a doença de Parkinson detecção usando recursos multimodais},
	year    = {2022},
}

@Article{HaqA2022,
	author  = {ul Haq A, Li JP, Agbley BLY, Mawuli CB, Ali Z, Nazir S},
	journal = {Sistemas Especialistas com Aplicações},
	title   = {Uma pesquisa de técnicas deaprendizado profundo com base em métodos de reconhecimento da doença de Parkinsonempregando dados clínicos.},
	year    = {2022},
}

@Article{Shafiq2022a,
	author  = {Shafiq S, Ahmed S, Kaiser MS, Mahmud M, Hossain MS, Andersson K},
	journal = {Acesso IEEE},
	title   = {Análise abrangente de algoritmos inspirados na natureza para o diagnóstico da doença deParkinson.},
	year    = {2022},
}

@Article{Shibata2022a,
	author  = {Shibata H, Uchida Y, Inui S, Kan H, Sakurai K, Oishi N},
	journal = {Parkinsonismo e Distúrbios Relacionados},
	title   = {Aprendizado de máquina treinado commapeamento quantitativo de suscetibilidade para detectar comprometimento cognitivo levena doença de Parkinson.},
	year    = {2022},
}

@Article{Thakur2023a,
	author  = {Thakur M, Dhanalakshmi S, Kuresan H, Senthil R, Narayanamoorthi R, Lai KW},
	journal = {Jornal de Inteligência Ambiental e Computação Humanizada},
	title   = {Classificador de máquina de Boltzmann restrito automatizado para diagnóstico precoce dadoença de Parkinson usando desenhos espirais digitalizados. Jornal de InteligênciaAmbiental e Computação Humanizada.},
	year    = {2023},
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@Article{LahmiriS2019,
	author  = {Lahmiri S, Shmuel A},
	journal = {. Processamento e controle de sinais biomédicos},
	title   = {Detecção da doença de Parkinson com base na classificação de padrões devoz e máquina de vetor de suporte otimizada.},
	year    = {2019},
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@Article{Ashour2020,
	author  = {Ashour AS, Nour MKA, Polat K, Guo Y, Alsaggaf W, El-Attar A},
	journal = {Acesso IEEE},
	title   = {Uma nova estrutura de doisníveis sucessivos de seleção de recursos usando procedimento baseado em peso paradetecção de perda de voz na doença de Parkinson.},
	year    = {2020},
}

@Article{SinghS2020,
	author  = {Singh S, Xu W},
	journal = {Telemedicina e e-Saúde.},
	title   = {Detecção robusta da doença de Parkinson usando colheita dados de voz do smartphone: uma abordagem de telemedicina.},
	year    = {2020},
}

@Article{Narendra2021,
	author  = {Narendra NP, Schuller B, Alku P},
	journal = {Transações IEEE/ACM em processamento de áudio, fala e linguagem},
	title   = {A detecção da doença de Parkinson a partir da fala usando informações de fonte de voz},
	year    = {2021},
}

@Article{SolanaLavalleG2021,
	author  = {Solana-Lavalle G, Rosas-Romero R.},
	journal = {Processamento e controle de sinais biomédicos.},
	title   = {Análise da voz como ferramenta auxiliar na detecção dadoença de Parkinson e sua subsequente interpretação clínica.},
	year    = {2021},
}

@Article{AroraS2021,
	author  = {Arora S, Tsanas A},
	journal = {Diagnósticos.},
	title   = {Avaliação da doença de Parkinson em escala usando Fala gravada por telefone: Insights da Iniciativa de Voz de Parkinson.},
	year    = {2021},
}

@Article{Hireys2022,
	author  = {Hireÿs M, Gazda M, Drot´ar P, Pah ND, Motin MA, Kumar DK},
	journal = {Computadores em biologia e medicina},
	title   = {convolucional Ensemble de rede neural para detecção da doença de Parkinson a partir de gravações de voz},
	year    = {2022},
}

@Article{Kaya2022,
	author  = {Kaya D},
	journal = {Simultaneidade e computação.},
	title   = {Detecção automatizada de gênero e doença de Parkinson ao mesmo tempo por meio de ummodelo profundo híbrido usando voz humana.},
	year    = {2022},
}

@Article{Zhang2023,
	author  = {Zhang T, Lin L, Xue Z},
	journal = {Sistemas especialistas com aplicações},
	title   = {Um método de extração de recursos de voz baseado na topologia de atributosfracionários para a detecção da doença de Parkinson.},
	year    = {2023},
}

@article{ozkanca2019depression,
	title={Depression screening from voice samples of patients affected by parkinson’s disease},
	author={Ozkanca, Yasin and G{\"o}ksu {\"O}zt{\"u}rk, Mira{\c{c}} and Ekmekci, Merve Nur and Atkins, David C and Demiroglu, Cenk and Hosseini Ghomi, Reza},
	journal={Digital biomarkers},
	volume={3},
	number={2},
	pages={72--82},
	year={2019},
	publisher={S. Karger AG}
}





@misc{misc_parkinsons_174,
	author       = {Little,Max},
	title        = {{Parkinsons}},
	year         = {2008},
	howpublished = {UCI Machine Learning Repository},
	note         = {{DOI}: https://doi.org/10.24432/C59C74}
}


@Article{Suppa2022,
	author   = {Suppa, Antonio and Costantini, Giovanni and Asci, Francesco and Di Leo, Pietro and Al-Wardat, Mohammad Sami and Di Lazzaro, Giulia and Scalise, Simona and Pisani, Antonio and Saggio, Giovanni},
	journal  = {Frontiers in Neurology},
	title    = {Voice in Parkinson's Disease: A Machine Learning Study},
	year     = {2022},
	issn     = {1664-2295},
	volume   = {13},
	abstract = {<sec>IntroductionParkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy.</sec><sec>MethodsWe investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations.</sec><sec>ResultsVoice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning.</sec><sec>ConclusionVoice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.</sec>},
	doi      = {10.3389/fneur.2022.831428},
	url      = {https://www.frontiersin.org/articles/10.3389/fneur.2022.831428},
}

@Article{Almeida2019,
	author   = {Jefferson S. Almeida and Pedro P. {Rebouças Filho} and Tiago Carneiro and Wei Wei and Robertas Damaševičius and Rytis Maskeliūnas and Victor Hugo C. {de Albuquerque}},
	journal  = {Pattern Recognition Letters},
	title    = {Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques},
	year     = {2019},
	issn     = {0167-8655},
	pages    = {55-62},
	volume   = {125},
	abstract = {This study investigates the processing of voice signals for detecting Parkinson’s disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.},
	doi      = {https://doi.org/10.1016/j.patrec.2019.04.005},
	keywords = {Parkinson’s disease, Speech processing, Phonological features, Feature extraction, Machine learning, Diagnosis},
	url      = {https://www.sciencedirect.com/science/article/pii/S0167865519301163},
}

@Article{Tillmann2020a,
	author    = {Tillmann, Ana Cristina and Swarowsky, Alessandra and Corr{\^e}a, Clynton Louren{\c{c}}o and Andrade, Alexandro and Moratelli, J{\'e}ssica and Boing, Leonessa and Vieira, Melissa de Carvalho Souza and Araujo, Camila da Cruz Ramos de and Guimar{\~a}es, Adriana Coutinho de Azevedo},
	journal   = {Arquivos de Neuro-Psiquiatria},
	title     = {Feasibility of a Brazilian samba protocol for patients with Parkinson's disease: a clinical non-randomized study},
	year      = {2020},
	pages     = {13--20},
	volume    = {78},
	publisher = {SciELO Brasil},
}




@Article{Mei2021,
	author    = {Mei, Jie and Desrosiers, Christian and Frasnelli, Johannes},
	journal   = {Frontiers in aging neuroscience},
	title     = {Machine learning for the diagnosis of Parkinson's disease: a review of literature},
	year      = {2021},
	pages     = {633752},
	volume    = {13},
	publisher = {Frontiers Media SA},
}





@Article{Byeon2021,
	author    = {Byeon, Haewon},
	journal   = {Applied Sciences},
	title     = {Comparing ensemble-based machine learning classifiers developed for distinguishing hypokinetic dysarthria from presbyphonia},
	year      = {2021},
	number    = {5},
	pages     = {2235},
	volume    = {11},
	publisher = {MDPI},
}

@Article{Pahuja2022,
	author    = {Pahuja, Gunjan and Prasad, Bhanu},
	journal   = {Computers in Biology and Medicine},
	title     = {Deep learning architectures for Parkinson's disease detection by using multi-modal features},
	year      = {2022},
	pages     = {105610},
	volume    = {146},
	publisher = {Elsevier},
}



@Article{Shafiq2022,
	author    = {Shafiq, Shakila and Ahmed, Sabbir and Kaiser, M Shamim and Mahmud, Mufti and Hossain, Md Shahadat and Andersson, Karl},
	journal   = {IEEE Access},
	title     = {Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis},
	year      = {2022},
	publisher = {IEEE},
}

@Article{Shibata2022,
	author    = {Shibata, Haruto and Uchida, Yuto and Inui, Shohei and Kan, Hirohito and Sakurai, Keita and Oishi, Naoya and Ueki, Yoshino and Oishi, Kenichi and Matsukawa, Noriyuki},
	journal   = {Parkinsonism \& Related Disorders},
	title     = {Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease},
	year      = {2022},
	pages     = {104--110},
	volume    = {94},
	publisher = {Elsevier},
}

@Article{Hemmerling2022,
	author   = {Daria Hemmerling and Magdalena Wojcik-Pedziwiatr},
	journal  = {Journal of Voice},
	title    = {Prediction and Estimation of Parkinson’s Disease Severity Based on Voice Signal},
	year     = {2022},
	issn     = {0892-1997},
	number   = {3},
	pages    = {439.e9-439.e20},
	volume   = {36},
	abstract = {Summary
	This paper presents the possibilities of using speech signal processing, analysis and regression methods in the context of assessment of neurological state in Parkinson’s disease patients up to 3 hours after taking medication which alleviates symptoms of the disease. The obtained results were used to create a system whose goals were the prognosis of values of selected acoustic parameters based on which it will be possible to further estimate a unified Parkinson’s disease rating scale score. For the experiment, we used the recordings of the vowel /a/ of 27 patients who were recorded 5 times each at a certain time after levodopa intake. The speech signal was parameterized, where in the acoustic parameters describing the signal were extracted and constituted input vectors to machine learning regression methods to search for characteristic diagnostic symptoms enabling automatic monitoring of the course of Parkinson’s disease. The results of the acoustic analysis were correlated with the clinical description and disease severity was assessed using the unified Parkinson’s disease rating scale. As a result, it was possible to create software which will support the work of the clinician in the field of therapy monitoring and provide a quantitative assessment of treatment results and a forecast of the effects of the therapy in short-term monitoring.},
	doi      = {https://doi.org/10.1016/j.jvoice.2020.06.004},
	keywords = {Signal processing, Voice, Parkinson’s disease, Prediction, Estimation of UPDRS score, Regression},
	url      = {https://www.sciencedirect.com/science/article/pii/S0892199720302319},
}

@Article{Karan2021,
	author    = {Karan, Biswajit and Sahu, Sitanshu Sekhar and Orozco-Arroyave, Juan Rafael and Mahto, Kartik},
	journal   = {Computer speech & language},
	title     = {Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction},
	year      = {2021},
	issn      = {0885-2308},
	pages     = {101216},
	volume    = {69},
	abstract  = {[Display omitted]
	•Time-frequency representation of voice signal with non-negative matrix factorization is proposed to extract relevant features for Parkinson disease (PD) detection.•The average classification accuracies of up to 92% in vowels and 97% in words are achieved.•In the dysarthria level evaluation, Spearman's correlations around 0.81 is achieved in sustained vowels and in isolated words.•The results indicate that the proposed approach is suitable and robust for the automatic detection of PD.
	Parkinson's disease (PD) is a neuron related disorder that affects the people in old age. The majority of people suffering from PD develop several voice impairments mainly related to what is known as dysarthric speech. Voice analysis can help in PD detection and in the evaluation of the dysarthria level of the patients. This study introduces time-frequency features to model discontinuities and abrupt changes that arise in the voice signal due to PD. The proposed method consists of four stages: time-frequency matrix (TFM) representation, TFM decomposition using non-negative matrix factorization (NMF), feature extraction and classification. Statistical analyses show that the proposed time-frequency features significantly differentiate between PD patients and healthy speakers. Experiments with sustained vowel phonations and isolated words of the corpus PC–GITA are conducted. The proposed method achieved average classification accuracies of up to 92% in vowels, and 97% in words. There is an improvement in accuracy ranging from 10% to 40% compared to existing methods. Further, the developed models are evaluated upon an independent dataset. Results on this separate test set show accuracies ranging from 63% to 75% in vowels, and from 53% to 75% in isolated words. Regarding the dysarthria level evaluation, Spearman's correlations between original and predicted labels are around 0.81 in sustained vowels and in isolated words. The results indicate that the proposed approach is suitable and robust for the automatic detection of PD.},
	copyright = {2021 Elsevier Ltd},
	keywords  = {Dysarthric speech ; Non-negative matrix factorization ; Parkinson's disease ; Spectrogram ; STFT ; Time-frequency},
	language  = {eng},
	publisher = {Elsevier Ltd},
}

@Article{Arora2021,
	author         = {Arora, Siddharth and Tsanas, Athanasios},
	journal        = {Diagnostics},
	title          = {Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson’s Voice Initiative},
	year           = {2021},
	issn           = {2075-4418},
	number         = {10},
	volume         = {11},
	abstract       = {Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker.},
	article-number = {1892},
	doi            = {10.3390/diagnostics11101892},
	pubmedid       = {34679590},
	url            = {https://www.mdpi.com/2075-4418/11/10/1892},
}

@Article{SolanaLavalle2021,
	author    = {Solana-Lavalle, Gabriel and Rosas-Romero, Roberto},
	journal   = {Biomedical signal processing and control},
	title     = {Analysis of voice as an assisting tool for detection of Parkinson's disease and its subsequent clinical interpretation},
	year      = {2021},
	issn      = {1746-8094},
	pages     = {102415},
	volume    = {66},
	abstract  = {[Display omitted]
	•Voice-based analysis for Parkinson's disease detection along with framing information.•An improvement, over previous works, is shown, in terms of performance detection and complexity.•The methodology is based on feature selection, four classifiers, and principal component analysis.•Features, with the highest contribution to Parkinson's disease detection, depend on gender.•An analysis of feature variability is important to understand the detection outcome.
	In this work, a voice-based analysis is conducted with the contribution of providing physicians with a decision tool along with framing information to help them see functional differences and understand why the detection method suspects PD. The voice-based detection method consists in applying feature subset selection and four different classifiers to voice recordings from five datasets (gender-based, balanced and unbalanced) derived from the largest public dataset for voice-based PD detection. One of the contributions is an improvement over previous works on voice-based PD detection over the same dataset, in terms of performance and complexity. The detection performance is characterized by 95.9% of accuracy, 98.35% of sensitivity, 91.06% of specificity, and 95.6% of precision in women; and 94.36% of accuracy, 100% of sensitivity, 97.1% of specificity, and 96.83% of precision in men. The number of features, fed to classifiers, ranges from 6 to 20. This work shows that different factors are associated with PD detection according to gender: high-frequency voice content is the most significant functional information to assist PD detection in women, while low-frequency content assists PD detection in men better. It is shown that a comparison of the variability of the most important features between patients with PD and controls can be used as contextual information by a physician to have a better interpretation of the classification.},
	copyright = {2021 The Author(s)},
	keywords  = {Assisting tool for diagnosis ; Feature subset selection ; Parkinson's disease detection ; Voice analysis},
	language  = {eng},
	publisher = {Elsevier Ltd},
}

@Article{Tracy2020a,
	author    = {Tracy, John M. and Özkanca, Yasin and Atkins, David C. and Hosseini Ghomi, Reza},
	journal   = {Journal of biomedical informatics},
	title     = {Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease},
	year      = {2020},
	issn      = {1532-0464},
	pages     = {103362--103362},
	volume    = {104},
	abstract  = {[Display omitted]
	•Mild Parkinson’s can be distinguished from no Parkinson’s using voice features.•Identity confounding leads to overestimates of model performance.•Gradient boosted model outperforms Random Forest and Logistic Regression models.
	Voice technology has grown tremendously in recent years and using voice as a biomarker has also been gaining evidence. We demonstrate the potential of voice in serving as a deep phenotype for Parkinson’s Disease (PD), the second most common neurodegenerative disorder worldwide, by presenting methodology for voice signal processing for clinical analysis. Detection of PD symptoms typically requires an exam by a movement disorder specialist and can be hard to access and inconsistent in findings. A vocal digital biomarker could supplement the cumbersome existing manual exam by detecting and quantifying symptoms to guide treatment. Specifically, vocal biomarkers of PD are a potentially effective method of assessing symptoms and severity in daily life, which is the focus of the current research. We analyzed a database of PD patient and non-PD subjects containing voice recordings that were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. The results are presented here and the limitations are discussed given the nature of the recordings. We note that our methodology only advances biomarker research and is not cleared for clinical use. Specifically, we demonstrate that conventional machine learning models applied to voice signals can be used to differentiate participants with PD who exhibit little to no symptoms from healthy controls. This work highlights the potential of voice to be used for early detection of PD and indicates that voice may serve as a deep phenotype for PD, enabling precision medicine by improving the speed, accuracy, accessibility, and cost of PD management.},
	address   = {United States},
	copyright = {2019},
	keywords  = {Audio features ; Deep phenotype ; Feature selection ; Parkinson's disease ; Voice biomarkers ; Voice technology},
	language  = {eng},
	publisher = {Elsevier Inc},
}


@Article{MoroVelazquez2021,
	author    = {Moro-Velazquez, Laureano and Gomez-Garcia, Jorge A. and Arias-Londoño, Julian D. and Dehak, Najim and Godino-Llorente, Juan I.},
	journal   = {Biomedical signal processing and control},
	title     = {Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects},
	year      = {2021},
	issn      = {1746-8094},
	pages     = {102418},
	volume    = {66},
	abstract  = {Parkinson's Disease (PD) affects speech in the form of dysphonia and hypokinetic dysarthria. Multiple studies have evaluated PD's influence on different aspects of speech, showing differences between speakers with and without PD. Most recent studies are focused on the proposal of new automatic and objective tools to help in the diagnosis and severity assessment. This comprehensive review identifies the most common features and machine learning techniques employed in automatically detecting and assessing the severity of PD using phonatory and articulatory aspects of speech and voice. We discuss their discriminant properties and literature findings as well as identify common methodological issues that can potentially bias results. The objective is to provide a broad overview of these methods, their advantages and disadvantages, and to identify the most promising methodologies to be explored in future works. We conclude that there is clear evidence that the articulatory and phonatory aspects of speech and voice are relevant for the automatic detection and severity assessment of PD. However, there is no standard methodology sufficiently validated in a clinical trial, and further research is required, especially to develop larger corpora and identify new objective biomarkers.},
	copyright = {2021 Elsevier Ltd},
	keywords  = {Articulation ; Automatic assessment ; Automatic detection ; Machine learning ; Medical research ; Medicine Experimental ; Parkinson's disease ; Phonation ; Voice and speech},
	language  = {eng},
	publisher = {Elsevier Ltd},
}



@Article{Karaman2021,
	author    = {Karaman, Onur and Çakın, Hakan and Alhudhaif, Adi and Polat, Kemal},
	journal   = {Expert systems with applications},
	title     = {Robust automated Parkinson disease detection based on voice signals with transfer learning},
	year      = {2021},
	issn      = {0957-4174},
	pages     = {115013},
	volume    = {178},
	abstract  = {•The CNN model was developed for automated PD identification based on voice characteristics.•The developed CNN model can successfully identify the PD with an accuracy of 91.17%.•It will be possible to detect PD by implementing the proposed CNN model to a smart device for personal usage.•The physician can utilize the developed model for PD detection during the in-clinic assessment.
	Parkinson's disease (PD) is a progressive-neurodegenerative disorder that affects more than 6 million people around the world. However, conventional techniques for PD detection are often hand-crafted, in which special expertise is needed. In this study, considering the importance of rapid diagnosis of the disease, it was aimed to develop deep convolutional neural networks (CNN) for automated PD identification based on biomarkers-derived voice signals. The developed CNN methods consisted of two main stages, including data pre-processing and fine-tunning-based transfer learning steps. To train and evaluate the performance of the developed model, datasets were collected from the mPower Voice database. SqueezeNet1_1, ResNet101, and DenseNet161 architectures were retrained and evaluated to determine which architecture can classify frequency-time information most accurately. The performance results revealed that the proposed model could successfully identify the PD with an accuracy of 89.75%, sensitivity of 91.50%, and precision of 88.40% for DenseNet-161 architecture identified as the most suitable fine-tuning architecture. The results revealed that the proposed model based on transfer learning with a fine-tuning approach provides an acceptable detection of PD with an accuracy of 89.75%. The outcomes of the study confirmed that by integrating the developed model into smart electronic devices, it will be able to develop alternative pre-diagnosis methods and will assist the physicians for PD detection during the in-clinic assessment. The success of the proposed model would imply an enhancement in the life quality of patients and a cost reduction for the national health system.},
	address   = {New York},
	copyright = {2021 Elsevier Ltd},
	keywords  = {Acoustic sensing ; Analysis ; Artificial neural networks ; Automation ; Biomarkers ; Convolutional neural network (CNN) ; Diagnosis ; Electronic devices ; Learning ; Neural networks ; Parkinson's disease ; Parkinson's disease (PD) ; Parkinsons disease ; Performance evaluation ; Physicians ; Transfer Learning ; Voice recognition ; Voice signal},
	language  = {eng},
	publisher = {Elsevier Ltd},
}



@Article{Hires2022,
	author    = {Hireš, Máté and Gazda, Matej and Drotár, Peter and Pah, Nemuel Daniel and Motin, Mohammod Abdul and Kumar, Dinesh Kant},
	journal   = {Computers in biology and medicine},
	title     = {Convolutional neural network ensemble for Parkinson's disease detection from voice recordings},
	year      = {2022},
	issn      = {0010-4825},
	pages     = {105021--105021},
	volume    = {141},
	abstract  = {The computerized detection of Parkinson's disease (PD) will facilitate population screening and frequent monitoring and provide a more objective measure of symptoms, benefiting both patients and healthcare providers. Dysarthria is an early symptom of the disease and examining it for computerized diagnosis and monitoring has been proposed. Deep learning-based approaches have advantages for such applications because they do not require manual feature extraction, and while this approach has achieved excellent results in speech recognition, its utilization in the detection of pathological voices is limited. In this work, we present an ensemble of convolutional neural networks (CNNs) for the detection of PD from the voice recordings of 50 healthy people and 50 people with PD obtained from PC-GITA, a publicly available database. We propose a multiple-fine-tuning method to train the base CNN. This approach reduces the semantical gap between the source task that has been used for network pretraining and the target task by expanding the training process by including training on another dataset. Training and testing were performed for each vowel separately, and a 10-fold validation was performed to test the models. The performance was measured by using accuracy, sensitivity, specificity and area under the ROC curve (AUC). The results show that this approach was able to distinguish between the voices of people with PD and those of healthy people for all vowels. While there were small differences between the different vowels, the best performance was when/a/was considered; we achieved 99% accuracy, 86.2% sensitivity, 93.3% specificity and 89.6% AUC. This shows that the method has potential for use in clinical practice for the screening, diagnosis and monitoring of PD, with the advantage that vowel-based voice recordings can be performed online without requiring additional hardware.
	•We present end-to-end trained convolutional neural network for identification of PD from voice recordings.•We propose multiple-fine-tuning approach that reduces the semantical gap between the source task and the target task.•The proposed approach was able to distinguish between the voices of people with PD and healthy people for all the vowels.},
	address   = {United States},
	copyright = {2021 Elsevier Ltd},
	keywords  = {Accuracy ; Analysis ; Artificial neural networks ; Automatic voice analysis ; CNN ensemble ; Convolutional neural network ; Data mining ; Databases Factual ; Datasets ; Deep learning ; Diagnosis ; Disease detection ; Dopamine ; Dysarthria ; Feature extraction ; Health care industry ; Humans ; Linguistics ; Machine learning ; Monitoring ; Movement disorders ; Neural networks ; Neural Networks Computer ; Neurodegenerative diseases ; Parkinson Disease - diagnosis ; Parkinson's disease ; Parkinsons disease ; Sensitivity ; Signs and symptoms ; Speech ; Speech recognition ; Support vector machines ; Telemedicine ; Training ; Transfer learning ; Tremor (Muscular contraction) ; Trends ; Voice ; Voice recognition ; Vowels},
	language  = {eng},
	publisher = {Elsevier Ltd},
}



@Article{Singh2020,
	author    = {Singh, Sanjana and Xu, Wenyao},
	journal   = {Telemedicine and e-Health},
	title     = {Robust detection of Parkinson's disease using harvested smartphone voice data: A telemedicine approach},
	year      = {2020},
	number    = {3},
	pages     = {327--334},
	volume    = {26},
	publisher = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…},
}

@Misc{Little2008,
	author       = {Little,Max},
	howpublished = {UCI Machine Learning Repository},
	note         = {{DOI}: https://doi.org/10.24432/C59C74},
	title        = {{Parkinsons}},
	year         = {2008},
}

@Book{Hastie2009,
	author    = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
	publisher = {Springer},
	title     = {The Elements of Statistical Learning: Data Mining, Inference, and Prediction},
	year      = {2009},
}

@Article{Breiman2001,
	author    = {Breiman, Leo},
	journal   = {Machine Learning},
	title     = {Random Forests},
	year      = {2001},
	number    = {1},
	pages     = {5--32},
	volume    = {45},
	publisher = {Springer},
}

@InProceedings{Chen2016,
	author       = {Chen, Tianqi and Guestrin, Carlos},
	booktitle    = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	title        = {XGBoost: A Scalable Tree Boosting System},
	year         = {2016},
	organization = {ACM},
	pages        = {785--794},
}

@Article{Cover1967,
	author    = {Cover, Thomas and Hart, Peter},
	journal   = {IEEE Transactions on Information Theory},
	title     = {Nearest neighbor pattern classification},
	year      = {1967},
	number    = {1},
	pages     = {21--27},
	volume    = {13},
	publisher = {IEEE},
}

@Comment{jabref-meta: databaseType:bibtex;}
