# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 3 Results
### Experiment I/III: Predict Health Status Based Solely on MoCA Score  
The first experiment targeted the prediction of whether a participant is healthy based only on the total MoCA score. In predicting PD patients (0) using the MoCA score alone, the RSES model demonstrated effectiveness with total accuracy of 80% across all cases it evaluated, presenting its capability to classify individuals accurately (Table 2 and Figure 2). Specifically, the model's performance when identifying healthy subjects (1) was perfect, with a 100% accuracy rate, showing its strength in recognizing individuals without PD. However, the specificity, or the model's ability to correctly identify healthy individuals as healthy, was calculated as 57.13%.  
To validate, we used hyperparameter-tuned and cross-validated ML models, including Logistic Regression, Support Vector Machine (SVM), and Random Forest. Interestingly, all three models reported a 60% accuracy, which is 20% lower than the RSES model, showing a lesser ability to generalize the prediction of health status based on MoCA scores alone. When predicting health status using only the MoCA score, all ML models achieved  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted**} \\ \cline{3-6}  & & **0** & **1** & **No. of Obj.** & **Accuracy** & **Coverage** \\ \hline \multirow{2}{*}{**Actual**} & 0 & 2.67 & 2.00 & 4.67 & 0.607 & 1.000 \\ \cline{2-6}  & 1 & 0 & 5.33 & 5.33 & 1.000 & 1.000 \\ \hline \multicolumn{6}{c}{**True positive rate**} & 1.00 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for predicting participant status (is_healthy = 0/1) based on the MoCA score using the RSES model, detailing the total number of tested objects (10), total accuracy (0.8), and coverage (1.0).  
Figure 1: Correlation matrix. The similarity of all paired parameters is correlated with the color of the cell in the matrix.  
a precision of 50%. This indicates that half of the positive predictions were incorrect, misclassifying healthy individuals as PD patients.  
The recall of 100% across models suggests that all PD cases were correctly identified, echoing the RSS's true positive rate but contrasting with its overall accuracy. The consistent confusion matrix results (2 TN, 4 FP, 0 FN, 4 TP) demonstrate the models' tendency to misclassify healthy controls as PD patients, thus presenting specificity issues. The specificity for the Logistic Regression, Support Vector Machine, and Random Forest models in Experiment I/III is all 33.3%. This indicates that each model correctly identified 33.3% of the actual negatives (healthy subjects) as being healthy. This highlights a challenge in distinguishing between PD patients and healthy controls based solely on the MoCA score in this experimental setup.  
In summary, the RSS model outperformed traditional ML methods in terms of accuracy in this specific experiment, presenting a potential in handling the predictive task with a limited set of features (MoCA score alone) (Table 3). High recall across all methods highlights a common strength in identifying PD cases but also underscores a shared weakness in specificity, particularly evident in the ML methods where false positives were a significant issue.