## 3 Results
& 90.32\% & 0.903 & 0.05 & 0.903 & 0.903 & 0.902 & 0.856 & 0.965 & 0.944 \\ \cline{2-11}  & & IG & 89.25\% & 0.892 & 0.055 & 0.892 & 0.892 & 0.891 & 0.84 & 0.966 & 0.936 \\ \cline{2-11}  & & GR & 88.71\% & 0.887 & 0.059 & 0.887 & 0.887 & 0.886 & 0.831 & 0.961 & 0.935 \\ \cline{2-11}  & & GA & **93.01\%** & **0.93** & **0.036** & **0.931** & **0.93** & **0.93** & **0.896** & **0.969** & **0.949** \\ \hline \multirow{8}{*}{N} & \multirow{3}{*}{NB} & Corr. & 93.01\% & 0.93 & 0.037 & 0.93 & 0.93 & 0.93 & 0.894 & 0.992 & 0.986 \\ \cline{2-11}  & & IG & 91.94\% & 0.919 & 0.042 & 0.919 & 0.919 & 0.919 & 0.877 & 0.985 & 0.966 \\ \cline{2-11}  & & GR & 93.01\% & 0.93 & 0.037 & 0.93 & 0.93 & 0.93 & 0.893 & 0.989 & 0.98 \\ \cline{2-11}  & & GA & **99.46\%** & **0.995** & **0.003** & **0.995** & **0.995** & **0.995** & **0.992** & **0.998** & **0.996** \\ \cline{2-11}  & & Corr. & 87.63\% & 0.876 & 0.069 & 0.88 & 0.876 & 0.876 & 0.813 & 0.981 & 0.966 \\ \cline{2-11}  & & IG & 89.78\% & 0.898 & 0.056 & 0.899 & 0.898 & 0.898 & 0.845 & 0.982 & 0.969 \\ \cline{2-11}  & & GR & 90.86\% & 0.909 & 0.048 & 0.909 & 0.909 & 0.909 & 0.861 & 0.982 & 0.968 \\ \cline{2-11}  & & GA & **96.77\%** & **0.968** & **0.018** & **0.969** & **0.968** & **0.968** & **0.951** & **0.991** & **0.984** \\ \cline{2-11}  & & Corr. Table 4 reports the results of the statistical analyses performed on the obtained results which point out the statistical significance of the differences within classifiers and feature selectors. In the case of ML algorithms, the Iman and Davenport test was applied and the performance of classifiers was found to be statistically significant and not equivalent, with a \(p\)-value less than 0.0001. Comparing the classifiers against each other using [PERSON]'s  
Figure 4: Respective acoustic group of optimal selected features: number of features retained after selection for each main feature domain. Figure 5: Radar chart displaying the different mean of the top performing features for each class (ASD, healthy, and PD), normalized by the healthy class (orange unit circle). Notice how ASD and PD behave similarly. Test, the NB's performance was found to be statistically significantly better than RF with a \(p\)-value less than 0.05. In addition, the NB's performance was tested using [PERSON]'s test against MLP and was found to be statistically significantly better, with a \(p\)-value of 0.01. Finally, MLP was tested against RF using [PERSON]'s and was found to be statistically significantly better with a \(p\)-value between 0.01 and 0.025. In the case of vocal tests, the Iman and Davenport test was used and the performance of both vocal tests was found to be statistically significant and not equivalent, with a \(p\)-value of 0.01. In addition, the performance of the sentence was better than vowel/e/. In the case of feature selection methods, the Iman and Davenport test was applied and the performance of these methods was found to be statistically significant and not equivalent, with a \(p\)-value of 0.04. Comparing the feature selection methods head to head, the GA's performance was found to be better than that of IG using [PERSON]'s Test, with a \(p\)-value less than 0.05. Moreover, the performance of the GA was better than GR and Correlation using [PERSON]'s test, with a \(p\)-value of 0.05. However, when comparing, the performance of IG with GR, and Correlation with GR and IG, they were found to be statistically insignificant with \(p\)-values larger than 0.2. This suggests that their performance was similar.