## 3 Results  
The number of features needed to reach the maximum possible accuracies varied with respect to the vocal test, feature selection methods, and classifiers (Figure 1). The feature selection method that was a combination of the Correlation sentence MLP had the least number of features (52), whereas the Correlation Vowel/e/NB combination had the most (99). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number of Classifiers** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline q\({}_{0.05}\) & 1.96 & 2.343 & 2.569 & 2.728 & 2.85 & 2.949 & 3.031 & 3.102 & 3.164 \\ q\({}_{0.10}\) & 1.645 & 2.052 & 2.291 & 2.459 & 2.589 & 2.693 & 2.78 & 2.855 & 2.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: [PERSON]’s q-values with alpha = 0.05 and 0.10 according to the number of classifiers (k) considered in the statistical test. Figure 1: Optimal number of features to reach the best accuracy for each selection method and classifier. Abbreviations: MLP = Multi-layer Perceptron; GR = Gain Ratio; IG = Information Gain; GA = Genetic Algorithm; RF = Random Forest; NB = Naive Bayes. Figure 2 illustrates how the best accuracy was obtained using filter feature selection methods (Correlation, IG, GR). It demonstrates how the 10-fold cross-validation accuracy stopped increasing eventually while the number of selected features increased. In the case of vocal test vowel/e/, the best results were found when classifying by NB and feature selecting by GA. The 10-fold cross-validation accuracy reached 95.70%. Furthermore, when doing AUROC analysis for this ML model, it is evident that the PD class had the highest AUROC (0.997), followed by HSs (0.994), and ASD (0.992). The lowest results were found when classifying by RF and feature selecting by GR. The 10-fold cross-validation accuracy reached was 87.10%. In addition, the HSs had the highest AUROC (0.975), followed by PD (0.974), and ASD (0.961). Figure 3 shows an example of an obtained ROC curve. The results from the MLP classifier were relatively lower than NB and better than RF. Selecting features with GA, the MLP 10-fold cross-validation accuracy reached 93.01%. In addition, the HSs had the highest AUROC (0.991), followed by PD (0.977), and ASD (0.937). In the case of vocal test _sentence_, the best results were found when classifying by NB and feature selecting by GA. The ML model reached a 99.46% 10-fold cross-validation accuracy. In addition, by analyzing the ML model's ROC curves, it is evident that the PD class had the highest AUROC (1.00), followed by HSs (0.997), and ASD (0.997). The lowest results were found when classifying by RF and feature selecting by Correlation. The 10-fold  
Figure 2: Example of accuracy saturation while the number of features for non-wrapper selectors increases. Graphs show the number of features versus classification accuracy for each multi-class (PD vs. ASD vs. HS) classification (**a**) RF classifier with IG selector, vowel/e/; (**b**) NB classifier with Correlation selector, vowel/e/; (**c**) MLP classifier with GR selector, vowel/e/; (**d**) MLP classifier with GR selector, sentence. cross-validation accuracy reached was 87.63%. In addition, PD had the highest AUROC (0.991), followed by ASD (0.982), and HSs (0.973) (Figure 3). The results from the MLP classifier were relatively lower than NB and better than RF. Selecting features with GA, the MLP 10-fold cross-validation accuracy reached 98.39%. In addition, the PD had the highest AUROC (1.00), followed by HSs (0.981), and ASD (0.986). A further investigation into a selection of the top performing features, which allowed us to obtain the highest accuracy, obtained from the GA selection applied to the NB classifier on the sentence task, is presented in Table 2.  
A summary of pathological speech (PD and ASD) detection results for all classifiers and selectors can be found in Table 3.