# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 4 Discussion  
We examined the predictive accuracy of health status based on MoCA scores. Experiment I focused solely on MoCA scores, Experiment II added gender as a variable, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name and Hyper Tuned Parameters** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline RSES & 0.933 & 0.934 & 0.93 & 0.934 \\ \hline Logistic Regression \{’C’: 0.01\} & 0.900 & 0.800 & 1.00 & 0.833 \\ \hline SVM \{’C’: 0.01, ’gamma’: 0.01\} & 0.900 & 0.800 & 1.00 & 0.833 \\ \hline Random Forest \{’n_estimators’: 2\} & 0.900 & 1.000 & 0.75 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of model performance in Experiment III/III. Figure 4: Enhanced ROC curves for Logistic Regression, Support Vector Machine, and Random Forest models predicting health status using MoCA scores, IRT, and TTS. The Logistic Regression curve is covered by the Support Version Machine curve. 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). Experiment III included IRT and TTS, alongside MoCA scores. The inclusion of temporal variables, as presented in Experiment III, highlighted how behavioral and temporal data can significantly refine the models' predictive capabilities. Initially, experiments revealed surprising specificity issues, especially when models were relying solely on MoCA scores. Interestingly, this aligns with the findings about the specificity issues of the Montreal Cognitive Assessment. The literature specifically mentions that while MoCA is good at detecting dementia (high sensitivity), it also produces a high number of false positives (low specificity). This means it often incorrectly identifies people as having dementia when they do not. This issue was particularly raised by [PERSON] et al. (2021), in their systematic review. They analyzed the accuracy of the MoCA test in detecting dementia across seven studies involving 9422 participants ([PERSON] et al., 2021). Their findings highlighted a significant concern; over 40% of non-demented individuals were falsely identified as having dementia at the MoCA cut-off of less than 26 points. These findings are in line with the trends we saw in this study. Furthermore, [PERSON] et al. (2020) highlight the challenges in accurately identifying mild cognitive impairment in Parkinson's disease (PD-MCI) using MoCA ([PERSON] et al., 2021). They emphasize that factors such as age, gender, and education significantly influence the test's accuracy, complicating the early-stage diagnosis of PD-MCI. This reinforces the notion that MoCA, while useful, has limitations in distinguishing between PD-MCI and normal aging or other conditions, especially when demographic variables are significant. Collectively, insights from the literature validate the patterns we observed in this study and suggest a potential limitation of MoCA's effectiveness, particularly in diverse settings and populations, including the described web-based approach. Hence, this research advocates for the integration of additional assessment tools or parameters alongside MoCA to enhance diagnostic accuracy. This is significant in advancing the field of cognitive assessment, encouraging a more comprehensive approach that accounts for individual differences. The findings are particularly crucial for PD-MCI identification, and they show the need for a nuanced approach that considers demographic and behavioral factors. Accordingly, this research suggests that relying solely on MoCA may lead to misdiagnoses or overlooked cases in PD patients. Importantly, the insights gained from this study can extend to other cognitive assessment tools. They prompt a re-evaluation of current practices and encourage the development of more inclusive and accurate assessment strategies, particularly in digital health platforms.