# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 4 Results and Discussion
Test accuracy using random label assignment is shown in Table 3. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{3}{*}{**Classifier No.**} & \multirow{3}{*}{**Classifier Name**} & \multicolumn{4}{c|}{**Overfit Factor with Original Label**} \\ \cline{3-6}  & & \multicolumn{2}{c|}{**Male**} & \multicolumn{2}{c|}{**Female**} \\ \cline{3-6}  & & **MFCC** & **PSF** & **MFCC** & **PSF** \\ \hline
1 & Medium KNN & \(-0.003\) & \(0.021\) & \(0.023\) & \(0.022\) \\ \cline{2-6}
2 & Coarse KNN & \(-0.012\) & \(0.017\) & \(0.014\) & \(0.003\) \\ \cline{2-6}
3 & Cosine KNN & \(0.005\) & \(0.015\) & \(0.016\) & \(0.021\) \\ \cline{2-6}
4 & Linear SVM & \(-0.001\) & \(-0.005\) & \(0.009\) & \(0\) \\ \cline{2-6}
5 & Coarse Tree & \(0.012\) & \(0.037\) & \(0.017\) & \(-0.001\) \\ \cline{2-6}
6 & Coarse Gaussian SVM & \(-0.002\) & \(0.006\) & \(0.013\) & \(0.001\) \\ \cline{2-6}
7 & Medium Tree & \(0.016\) & \(0.031\) & \(0.015\) & \(0.003\) \\ \cline{2-6}
8 & Ensemble Boosted Tree & \(0.018\) & \(0.026\) & \(0.025\) & \(0.004\) \\ \cline{2-6}
9 & RUS Boosted Tree & \(0.016\) & \(0.031\) & \(0.015\) & \(0.003\) \\ \cline{2-6}
10 & Logistic Regression & \(0\) & \(-0.012\) & \(0.007\) & \(-0.001\) \\ \cline{2-6}
11 & Fine Tree & \(0.039\) & \(0.03\) & \(0.061\) & \(0.008\) \\ \cline{2-6}
12 & Medium Gaussian SVM & \(0.003\) & \(0.006\) & \(0.02\) & \(0.001\) \\ \cline{2-6}
13 & Fine KNN & \(0.035\) & \(0.057\) & \(0.109\) & \(0.111\) \\ \cline{2-6}
14 & Weighted KNN & \(0.026\) & \(0.055\) & \(0.099\) & \(0.1\) \\ \cline{2-6}
15 & Ensemble Bagged Trees & \(0.046\) & \(0.051\) & \(0.1\) & \(0.051\) \\ \cline{2-6}
16 & Ensemble Subspace KNN & \(0.026\) & \(0.139\) & \(0.089\) & \(0.13\) \\ \cline{2-6}
17 & Fine Gaussian SVM & \(0.173\) & \(0.017\) & \(0.137\) & \(0.008\) \\ \hline \end{tabular}
\end{table} 
Table 2: Overfit Factor with Original Label. Figure 8: Percentage reduction in classification performances with original and random labels—classification between MFCCs and PSF (Database1). In both Tables 2 and 3, the classifiers were arranged in ascending order of the overfit factor observed in males using MFCC for training. Index numbers were assigned to each classifier in the leftmost column of both tables for easy identification during discussions. To ensure robustness and bias-free analysis, all classifier experiments, including training, testing, and result acquisition, were repeated 10 times and the median values of overfit factors and test accuracies were then presented in Tables 2 and 3, respectively. In Table 2, for both MFCCs and PSFs in both genders, classifiers 1 to 10 exhibited significantly lower overfit factors compared to the rest. Conversely, from classifier 11 to classifier 17, the overfit factors increased, indicating a greater tendency to overfit the training data. Table 3 displays the median test accuracies for each classifier under pitch-synchronous segmentation when labels were randomized. Among the classifiers with lower overfit values identified in Table 2 (classifiers 1 to 10), the first three classifiers exhibited significantly higher accuracy values even with random labels. This finding indicates that kNNs are more suitable for speaker verification applications than PD classification. They tend to rely on the proximity of samples in the feature space and learn minimal information related to the effects of PD. Comparably, the results in Table 3 revealed that MFCCs achieved much higher accuracy values than PSFs, suggesting that MFCCs are better suited for speaker identification tasks compared to PD classification. Based on these observations, only classifiers 4-10 were identified as optimal classifiers. For the final step, training and testing were carried out using different databases. Database 1 and Database 2 consist of speakers with varying characteristics, including nationality, spoken language, paragraph length, and sampling frequency (16 kHz for Database 1 and 44.1 kHz for Database 2).