# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 3 Results
### Experiment III/III: Predict Health Status Based on MoCA Score and IRT + TTS  
The third experiment targeted the prediction of whether a participant is healthy based the (a) MoCA score, (b) IRT, and (c) TTS. This experiment examined whether the addition of temporal measures as a predictive variable would enhance the model's performance compared to using the MoCA score alone.  
Importantly, the RSES analysis with the inclusion of temporal values showed an increase in accuracy compared to the first experiment (80% to 93%). The detailed results indicate a high accuracy for different classes, with a significant true positive rate and higher accuracy for classifying PD patients (Table 6). The specificity for the RSES model in Experiment III/III is approximately 0.934. This indicates that the RSES model correctly identified 93.4% of the actual healthy individuals as being healthy, demonstrating a better performance in distinguishing between PD patients and healthy controls than based solely on MoCA scores, or MoCA scores together with gender.  
The addition of temporal measures as a predictive variable in Experiment III enhanced the classical model's performance as well. The Logistic Regression model achieved perfect sensitivity (100%) and showed an increase in overall accuracy (93%) and precision (80%). SVM maintained its sensitivity at 100% and showed an improvement in overall accuracy (90%) and precision (80%) with the inclusion of IRT and TTS. Random Forest showed a significant increase in performance across all metrics (accuracy = 90%, precision = 100%,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted**} & & \\ \cline{3-6}  & & **0** & **1** & **No. of Obj.** & **Accuracy** & **Coverage** \\ \hline \multirow{2}{*}{**Actual**} & 0 & 4.67 & 0.33 & 5 & 0.933 & 1.000 \\ \cline{2-6}  & 1 & 0.33 & 4.67 & 5 & 0.933 & 1.000 \\ \hline \multicolumn{6}{c}{**True positive rate**} & 0.93 & 0.93 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for predicting participant status (is_healthy = 0/1) based on the MoCA score, IRT, and TTS using the RSES model, detailing the total number of tested objects (10), total accuracy (0.93), and coverage (1.0).  
Figure 3: ROC curves for machine learning models predicting health status using MoCA scores and gender as features. The purple dashed line represents the baseline performance of a random classifier (random chance line).  
sensitivity = 75%) compared to the first and second experiments. The specificity for all ML models also increased. For the Logistic Regression and Support Vector Machine, this value increased to 83.3%, and Random Forest achieved a perfect (100%) specificity score.  
In summary, Experiment III analyzed the impact of adding Instrumental Reaction Time and Time to Submit to MoCA scores for prediction of health status. The RSES model achieved a 93% accuracy and sensitivity rate. Machine learning models like Logistic Regression, Support Vector Machine, and Random Forest also showed marked improvements in their predictive capabilities (Table 7 and Figure 4). Specifically, Random Forest demonstrated perfect specificity, suggesting that temporal measures significantly enhance the model's ability to correctly identify healthy individuals.  
The results from Experiment III seem very promising. The AUC values provided for the models in the ROC curves suggest near-perfect or perfect classification ability. Understandably, to confirm the model's predictive accuracy, additional validation using an independent dataset or through a longitudinal prospective study is recommended. We plan future studies with larger samples to confirm these findings and explore other variables that may influence the results.