# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 3 Results
### Experiment II/III: Predict Health Status Based on MoCA Score and Gender  
The second experiment targeted the prediction of whether a participant is healthy based on both the (a) MoCA score and (b) gender. This experiment examined whether the addition of gender as a predictive variable would enhance the model's performance compared to using the MoCA score alone. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name and Hyper Tuned Parameters** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline RSS & 0.8 & 0.727 & 0.86 & 0.572 \\ \hline Logistic Regression [’C’: 1] & 0.6 & 0.500 & 1.00 & 0.333 \\ \hline SVM [’C’: 0.1, ‘gamma’: 0.01] & 0.6 & 0.500 & 1.00 & 0.333 \\ \hline Random Forest [’n\_estimators’: 4] & 0.6 & 0.500 & 1.00 & 0.333 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of model performance in Experiment I/III. Figure 2: Comparative ROC curves for Logistic Regression, Support Vector Machine, and Random Forest models predicting health status from MoCA scores. The Logistic Regression curve (blue line) is covered by the Support Version Machine curve. The purple dashed line represents the baseline performance of a random classifier (random chance line). By focusing on gender, we wanted to develop models that could potentially identify health status indicators across more diverse and inclusive demographic profiles. This approach allows for the exploration of health determinants in settings where age may not be the primary factor of interest or where age information is not available. Interestingly, the RSES analysis with the inclusion of gender showed a decrease in accuracy compared to the first experiment (80% to 70%). The detailed results indicate a mixed accuracy for different classes, with a significant true positive rate (the ability to correctly identify healthy individuals) but a lower accuracy for classifying PD patients (Table 4). The specificity for the RSES model in Experiment II/III is approximately 0.875. This indicates that the RSES model correctly identified 87.5% of the actual healthy individuals as being healthy, demonstrating a better performance in distinguishing between PD patients and healthy controls than the model based solely on MoCA scores. Surprisingly, the addition of gender as a predictive variable in Experiment II did not enhance the classical model's performance as well. The Logistic Regression model, despite a high sensitivity (75%), showed a decrease in overall accuracy (50%) and precision (42.86%) when including gender, compared to its performance in the first experiment. SVM maintained its sensitivity at 100% but did not show an improvement in overall accuracy (60%) or precision (50%) with the inclusion of gender. Random Forest showed a significant decrease in performance across all metrics (accuracy = 40%, precision = 33.33%, sensitivity = 50%) compared to the first experiment. The specificity for the Logistic Regression, Support Vector Machine, and Random Forest models in Experiment II is 0.333, or 33.3%. This indicates that each model correctly identified 33.3% of the actual healthy individuals (label 1) as being healthy, highlighting a challenge across all models in this experimental setup (Table 5 and Figure 3). The results highlight the challenge in selecting features for predictive modeling, especially in small datasets. The inclusion of additional variables does not always lead to improved performance and may sometimes detract from the model's accuracy due to overfitting or the introduction of noise. To conclude, the addition of gender as a feature alongside MoCA scores does not seem to significantly improve the models' performance, especially in the case of Random Forest, where accuracy decreases. However, the RSES model demonstrates a better balance in sensitivity when predicting between PD patients and healthy individuals compared to