## 3 Proposed Methodology
### Data Preprocessing
The outcomes, as illustrated in Figure 6, showcase the statistical values derived from applying the proposed approach to two datasets, PD-DS-I and PD-DS-II. The results from this demonstration clearly indicate that the proposed method consistently achieved outstanding performance across both datasets. The analysis of variance (ANOVA) was conducted, yielding an F-score of 0.348 and a corresponding \(p\)-value of 0.794. As the \(p\)-value exceeds the significance threshold, we fail to reject the null hypothesis. Thus, there is no statistically significant difference observed between the groups. Figure 7 displays the confusion matrix for both datasets, utilized in calculating various performance metrics, and highlights the superior performance of the proposed technique. Additionally, the efficacy of the LightGBM classifier is further validated through the Area under the Receiver Operating Characteristics Curve (ROC-AUC). Figure 6: Performance measure of proposed work based on PD-DS-I and PD-DS-II. The ROC-AUC graphs for both PD-DS-I and PD-DS-II are illustrated in Figure 7, where the AUC values are recorded at 0.991 and 0.959, respectively. These high AUC values reinforce the effectiveness of the proposed approach in accurately distinguishing PD patients from healthy individuals. This accuracy is achieved by utilizing multimodal features extracted from EEG signals and MRI, demonstrating the proposed model's capability in PD diagnosis. The Confusion matrix is shown in Figure 8.

In a subsequent experiment, the performance of our proposed model was benchmarked against the studies by [PERSON] et al. and [PERSON] et al. This comparative analysis, as detailed in Table 6, unequivocally demonstrates that our approach significantly outperformed the baseline studies. Specifically, the proposed model exhibited an accuracy improvement of 11% on PD-DS-I and 10.2% on PD-DS-II, respectively, showcasing its superior diagnostic capabilities in PD detection. The ROC curves for PD-DS-1 and PD-DS-II are in Figure 9.

Figure 7: Confusion Matrix for LightGBM (**a**) EEG (**b**) MRI (**c**) Multimodal for PD-DS-I. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **MCC** & **AUC** & **Kappa Statistics** \\ \hline PD-DS-I Dataset & & & & & \\ \hline Proposed Approach & 97.02 & 97.28 & 0.97 & 0.96 & 0.97 \\ \hline [PERSON] et al. & 86.63 & 85.45 & 0.86 & 0.86 & 0.85 \\ \hline [PERSON] et al. & 92.36 & 92.63 & 0.92 & 0.91 & 0.91 \\ \hline PD-DS-II Dataset & & & & & \\ \hline Proposed Approach & 96.13 & 96.36 & 0.96 & 0.95 & 0.96 \\ \hline [PERSON] et al. & 89.63 & 88.45 & 0.89 & 0.89 & 0.89 \\ \hline [PERSON] et al. & 91.23 & 91.78 & 0.91 & 0.90 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of proposed work with machine learning models. Figure 8: Confusion Matrix for LightGBM (**a**) EEG (**b**) MRI (**c**) Multimodal for PD-DS-II. Figure 10 presents a comprehensive evaluation of various classifiers, focusing on classification accuracy, specificity, sensitivity, MCC, ROC, and the Kappa statistic. This analysis reveals that the proposed model achieves the highest overall classification performance. The proposed model's accuracy, sensitivity, and specificity, as obtained after implementing 10-fold cross-validation, are exceptionally high at 97.17%, 96.58%, and 96.82%, respectively. Moreover, the AUC value for the proposed work is 0.96, categorizing it as an almost perfect classification method. In contrast, while the Al-Khasawneh et al. classifier shows a slightly higher AUC of 0.86, its performance in other areas, such as classification specificity (85.45%), sensitivity (86.63%), and MCC (0.86), is lower compared to proposed work. Additionally, the Kappa statistic for this classifier is noted at 0.85, the highest among the classifiers reviewed, indicating near-perfect classification stability and a high degree of alignment between the classification and actual values. This suggests that the proposed work almost accurately classifies all test instances.