# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 5 Results and Discussion
The column \"\(t\)\" indicates the minimum number of decision trees deployed to achieve such maximum performances. Similarly, the number of features for each feature segment is denoted by \"\(m\)\". Overall, the performance of the decision forests presented in Tables 5-7 is far better than the performance of the same decision forests (with the default number of trees) outlined in Tables 2 and 3. For an instance, the detection accuracy observed by SysFor, Random Forest and ForestPA received are 82.54%, 88.24%, and 86.28% with the help of 7, 4, and 69 decision trees in the forests, respectively, which is 1.98%, 13.64%, and 8.5% higher than the decision forests deployed in its default setting. Moreover, the SysFor classifier gains the peak detection accuracy with 50% training instances only. The same implication can be observed for all feature segments. Therefore, it is evident that the dynamic allocation of decision trees shows far better results than the default number of decision trees. Since the focusing point is to present three Parkinson detection systems using the decision forests, it is essential to compare the performance of decision forests to understand the versatility of the forests towards [PERSON]'s detection. In this context, three popular performance measures, viz., detection accuracy, sensitivity and specificity utilized for better analysis of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature Segments** & **m** & **t** & **Tr/Ts** & **Acc** & **Prc** & **Sen** & **Spc** & **Fall** & **Miss** \\   Time Frequency & 11 & 4 & 80/20 & 88.24 & 0.88 & 0.88 & 0.74 & 0.26 & 0.12 \\   Baseline & 21 & 4 & 80/20 & 90.20 & 0.91 & 0.90 & 0.60 & 0.40 & 0.10 \\   Vocal Fold & 22 & 65 & 80/20 & 90.20 & 0.91 & 0.90 & 0.60 & 0.40 & 0.10 \\   MFCC & 84 & 19 & 80/20 & 94.12 & 0.94 & 0.94 & 0.83 & 0.17 & 0.06 \\   Wavelet & 182 & 4 & 80/20 & 86.28 & 0.86 & 0.86 & 0.66 & 0.34 & 0.14 \\   TQWT & 432 & 29 & 80/20 & 92.16 & 0.93 & 0.92 & 0.68 & 0.32 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Maximum performance received for Random Forest classifier with minimum number of decision trees on primary and derived features of Istanbul acoustic dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Feature Segments** & **m** & **t** & **Tr/Ts** & **Acc** & **Prc** & **Sen** & **Spc** & **Fall** & **Miss** \\   Time Frequency & 11 & 69 & 80/20 & 86.28 & 0.88 & 0.86 & 0.44 & 0.56 & 0.14 \\   Baseline & 21 & 6 & 80/20 & 92.16 & 0.93 & 0.92 & 0.68 & 0.32 & 0.08 \\   Vocal Fold & 22 & 22 & 80/20 & 88.24 & 0.9 & 0.88 & 0.52 & 0.48 & 0.12 \\   MFCC & 84 & 14 & 80/20 & 94.12 & 0.95 & 0.94 & 0.76 & 0.24 & 0.06 \\   Wavelet & 182 & 24 & 80/20 & 86.28 & 0.88 & 0.86 & 0.44 & 0.56 & 0.14 \\   TQWT & 432 & 11 & 80/20 & 92.16 & 0.92 & 0.92 & 0.75 & 0.25 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Maximum performance received for ForestPA classifier with minimum number of decision trees on primary and derived features of Istanbul acoustic dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature Segments** & **m** & **t** & **Tr/Ts** & **Acc** & **Prc** & **Sen** & **Spc** & **Fall** & **Miss** \\   Time Frequency & 11 & 7 & 50/50 & 82.54 & 0.83 & 0.83 & 0.49 & 0.51 & 0.18 \\   Baseline & 21 & 40 & 80/20 & 92.16 & 0.92 & 0.92 & 0.75 & 0.25 & 0.08 \\   Vocal Fold & 22 & 2 & 80/20 & 88.24 & 0.88 & 0.88 & 0.59 & 0.41 & 0.12 \\   MFCC & 84 & 53 & 80/20 & 92.16 & 0.93 & 0.92 & 0.91 & 0.1 & 0.08 \\   Wavelet & 182 & 18 & 80/20 & 88.24 & 0.88 & 0.88 & 0.67 & 0.33 & 0.12 \\   TQWT & 432 & 73 & 80/20 & 90.20 & 0.90 & 0.9 & 0.75 & 0.25 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum performance received for SysFor classifier with minimum number of decision trees on primary and derived features of Istanbul acoustic dataset. Figure 4 shows mixed results for all the decision forests. For Time Frequency, Vocal Fold, and MFCC, the Random Forest shows best detection rate. Similarly, SysFor shows better results for Wavelet feature segments.