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\chapter{Estado da Arte}
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\section{Introdução}

Early detection of Parkinson's disease is essential to ensure the best possible treatment and better quality of life for patients. The use of voice analysis techniques has been an area of growing interest for the detection of the disease. In this section, we will present some related works that address the detection of Parkinson's disease through voice analysis. For the selection of these works, references were sought in the main scientific databases, such as Web of Science, Elsevier, MDPI, Scopus, Science Direct, IEEE Xplore, Springer and Google Scholar.

Many articles have used voice analysis to detect PD (cite). it's occours becouse the voice impairment is one of the first symptoms to appear in the patiente(cite). Voice analysis is used as a non-invasive method to identify early signs of PD. Various algorithms and machine learning techniques are applied to analyze specific voice characteristics that may be affected by the disease(cite).

In a survey by \cite{rana2022}, the authors the effectiveness of using supervised classification algorithms, such as support vector machine (SVM), naïve Bayes, k-nearest neighbor (K-NN), and artificial neural network (ANN) . In their study, they used several parameters such as jitter, fundamental frequency, pitch, shimmer, and other statistical measures. The best accuracy among these classifiers was obtained from KNN with a 93.83\% accuracy rate and it also provided good performance in other parameters, such as sensitivity, specificity, and error rate.The experimental outcomes show that the accuracy of SVM is 87.17\%, naïve Bayes is 74.11\%, ANN is 96.7\%, and KNN is 87.17\%, and it is concluded that the ANN is the most accurate one with the highest accuracy.

In \cite{ouhmida2021}, aims to investigated and evaluated the performance of  three classification methods: Support Vector Machine (SVM), K-Nearest-Neighbors  (KNN), and Decision Tree (DT). Their approach was based on feature learning for an  automatic identification of Parkinson’s Disease using voice recording analysis. The simulation results show that the KNN algorithm yielded the best classifier performance in term of accuracy and reached an AUC of 98.26\%.

In on article propose by \cite{bios22}, a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’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’s correlation coefficients. 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.

In many works\cite{nissar2019,yaman2020,hussain2022}, explore the possibilities of a deep neural network (DNN) and  long short-term memory (LSTM) network-based model for predicting Parkinson's disease using a  subject’s voice samples. The various simulations were performed on the dataset to exhibit the efficacy  of the models along with their comparison to the conventional machine learning techniques. The  results obtained show high values of various metrics including an accuracy of 97.12\% and 99.03\%  for DNN and LSTM respectively which strongly suggest their efficiency for the detection of PD.

In the study by \cite{Polat2019}, a hybrid method for the detection of Parkinson's disease with the transmitted characteristics of speech signals was proposed. The SMOTE (Synthetic Minority Over-Sampling Technique) method was used to transform this unbalanced dataset into a balanced dataset and then Random Forests was applied to classify the Parkinson's disease dataset. Only the classification of random forests scored 87.037\% in the classification of the PD dataset, while the standard hybrid method (the combination of SMOTE and random forests) achieved 94.89\% classification success. In the work of \cite{Nahar-etal2021}, used Boruta, Recursive Resource Elimination (RFE) and Random Forest Classifier (RF) to detect Parkinson's disease, with the diagnosis of Parkinson's consumed 82.35\% of accuracy. In the case of the \cite{Avuclu2020}, was used algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed and the best test result was obtained from Random Forest classifier with 85.81\%.

In the study by \cite{Solana-Lavalle2020}, four classifiers was used (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.

The authors \cite{Lamba2022} have proposed a hybrid MIRFE feature selection approach based on mutual information gain and recursive feature elimination methods. The proposed feature selection approach is compared with the five standard feature selection methods by random forest and XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features, with a feature reduction ratio of 94.69\%. An accuracy of 93.88\% and an area under the curve (AUC) of 0.978 are obtained by the proposed system.

The model-based logic strategies used by \cite{Yadav2021} include logistic regression (LR), k nearest neighbors (k-NN), support vector classifier (SVC), gradient boosting classifier (GBC), and random forest classifier (RF). The estimated reliability, like the ROC curve and confusion matrix, is five-fold cross-validation. Was construct another model that depends on the ensemble method and utilization of majority voting, weighted average, bagging, Ada\_boost and Gradient\_boosting. Among the five algorithms, the higher accuracy fluctuates in the middle of 70–95\%. Among them, SVC obtains 93.83\% accuracy from the five basic classifiers, and Bagging obtains 73.28\% accuracy from the ensemble technique.


\section{Conclusão}
os dados obtidos são copilados na tabela \ref
	\begin{table}[!ht]
		\centering
		\caption{Tabela de resumo dos dados do estado daarte pesquisado}
\begin{adjustbox}{max width=\textwidth}
	\begin{tabular}{lllllll}
			\hline
			Autor&Método&Acurácia&F1-score&Sensibilidade&Especificidade&Precisão\\
			\hline
     		autor 1 &SVM,K-NN,ANN & 93,83\% - & - & -& -&\\
			autor 2 &SVM,Decision Tree, K-NN & 98,26\% - & & - &- &\\ 
			\hline
	\end{tabular}
\end{adjustbox}
\label{tab:resultEA}
\end{table}




