# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 4 Results and Discussion
Figure 9 presents the results obtained when the optimal set of classifiers is trained and tested over different databases after applying normalization and using the z-scores before training the classifiers. The normalization process ensured that the classifiers were trained and tested on data with consistent scales, facilitating fair and reliable performance comparisons across the two databases. Figure 10 shows the results for the same grouping as Figure 9, where the syllabic level covariances are used for classification instead of the z-scores. Comparison between both figures shows the MFCCs cannot contain the effects \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Classifier No.**} & \multirow{2}{*}{**Classifier Name**} & \multicolumn{4}{c|}{**Median Test Accuracy**} \\ \cline{3-6}  & & \multicolumn{2}{c|}{**Male**} & \multicolumn{2}{c|}{**Female**} \\ \cline{3-6}  & & **MFCC** & **PSF** & **MFCC** & **PSF** \\ \hline
1 & Medium KNN & 90.18 & 75.77 & 93.73 & 78.725 \\
2 & Coarse KNN & 85.33 & 72.17 & 89.11 & 74.855 \\
3 & Cosine KNN & 89.46 & 75.965 & 93.14 & 79.31 \\
4 & Linear SVM & 67.69 & 58.8 & 80.44 & 53.65 \\
5 & Coarse Tree & 66.52 & 59.52 & 77.01 & 69.605 \\
6 & Coarse Gaussian SVM & 75.12 & 61.715 & 83.92 & 64.525 \\
7 & Medium Tree & 68.83 & 63.09 & 76.81 & 72.405 \\
8 & Ensemble Boosted Tree & 74.04 & 67.96 & 82.76 & 75.415 \\
9 & RUS Boosted Tree & 68.83 & 63.09 & 76.81 & 72.35 \\
10 & Logistic Regression & 67.62 & 59.335 & 79.92 & 59.38 \\
11 & Fine Tree & 72.18 & 66.965 & 80.62 & 75.93 \\
12 & Medium Gaussian SVM & 86.63 & 70.21 & 90.78 & 75.865 \\
13 & Fine KNN & 91.27 & 75.32 & 93.91 & 76.43 \\
14 & Weighted KNN & 91.97 & 77.4 & 94.29 & 78.66 \\
15 & Ensemble Bagged Trees & 88.67 & 80.98 & 92.39 & 85.26 \\
16 & Ensemble Subspace KNN & 90.62 & 66.125 & 94.08 & 73.915 \\
17 & Fine Gaussian SVM & 92.14 & 78.415 & 95.86 & 80.62 \\ \hline \end{tabular}
\end{table} 
Table 3: Median Test Accuracies with Random Labels in Both Genders for Both Features. of PD as well as PSFs. Covariances computed over PSFs at the super-segment level are more reliable than MFCCs. While it can clearly be improved further, these results provide evidence for the need to develop features that capture the vocalic dynamics more than the features that are reliable in identifying the speakers. Besides accuracy, additional metrics were used to evaluate the performance. The descriptions are as follows:  
True Positives (_TP_): Number of PD samples predicted as PD. True Negatives (_TN_): Number of HC samples predicted as HC. False Positives (_FP_): Number of HC samples predicted as PD. Figure 10: Classification accuracies with Database 1 used for training and Database 2 used for testing using covariances from syllabic analysis z-scores for MFCCs and PSF. Figure 9: Classification accuracies with Database 1 used for training and Database 2 used for testing using z-scores for MFCCs and PSF. False Negatives (\(FN\)): Number of PD samples predicted as HC. Accuracy: Proportion of test samples correctly predicted. \[\text{Accuracy}=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{4}\]  
Precision (P): Proportion of PD predictions that were correct. \[\text{Precision}=\frac{TP}{(TP+FP)} \tag{5}\]  
Recall (R): Proportion of all PD samples correctly predicted. \[\text{Recall}=\frac{TP}{(TP+FN)} \tag{6}\]  
F1-Score: Harmonic mean of precision and recall. \[\text{F1}-\text{Score}=\frac{2\times P\times R}{(P+R)} \tag{7}\]  
Matthews Correlation Coefficient (MCC): An improvement over F1-Score as it includes the TN in its computation. \[\text{MCC}=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{8}\]  
ROC-AUC: Area under Receiver Operating Characteristic (ROC) curve. All the metrics except MCC have values between 0 and 1, with 1 being the best possible value. MCC can have values between \(-1\) and \(1\), with 1 being the best possible value.