## 3 Proposed Methodology
### Data Preprocessing
The first dataset, abbreviated as PD-DS-I, has been obtained from the Latin American Brain Health Institute (BrainLat) and has unveiled a significant multimodal neuroimaging dataset comprising 780 participants. This collection features 530 individuals diagnosed with PD and 250 healthy controls (HCs). The very next dataset (PD-DS-II) is a collection of anatomical MRI that directly identifies neuronal loss in PD. Exploring changes in functional connectivity (FC) offers a promising route to develop non-invasive and radiation-free neuroimaging markers for the disease. The PD-DS-II utilizes data from two sources: the Neurocon dataset, which includes 127 PD patients and 16 age-matched healthy controls, and the Tao Wu dataset, comprising 220 PD patients and 20 age-matched controls. Both datasets provide T1-weighted and resting-state scans. ### Performance Measures

To assess the stability and reliability of the proposed method, we employ a 10-fold cross-validation (CV) procedure. This approach mitigates the risks of both underfitting and overfitting and helps reduce the variance when analyzing results. In this process, the feature vector is split into ten random subsets. In each fold, nine subsets are used for training, while the remaining one is used for testing. After 10 iterations (folds), the training algorithm's performance measures are averaged. The proposed model learning rate is 0.001, with a batch size of 32 and training over around 50 epochs. A dropout rate of 0.5 is often applied for regularization, along with weight initialization. The outcome of this process is a confusion matrix that provides values for true negative (TN), true positive (TP), false negative (FN), and false positive (FP). Here, TP and TN represent correct predictions, where Parkinson's patients and healthy controls are accurately identified. Conversely, FP and FN indicate incorrect predictions, marking individuals as having Parkinson's when they do not (FP) and vice versa (FN). Performance metrics are crucial for evaluating the effectiveness of our study in detecting PD patients. Additionally, we measure computational time to assess the real-time applicability of the classifier. Each of these measures is explained in brief as follows:

* **Accuracy:** This metric determines the overall effectiveness of a classifier in correctly distinguishing between negative and positive cases. It reflects the proportion of true results in relation to all the cases examined. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (9)
* **Sensitivity:** This metric assesses the classifier's ability to correctly identify positive cases. It indicates the proportion of actual positives (true positives) that are correctly identified by the classifier, reflecting its effectiveness in detecting cases where the condition is present. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Nature of Data** & **Age Group** & **Class Label** \\ \hline PD-DS-I & Multimodal EEG and MRI & Avg. 67 & \\  & & [Ranging from 21–89] & PD patient 530 & Non-PD patient 250 \\ \hline PD-DS-II & Multimodal EEG and MRI & Avg. 50 & \\  & & [Ranging from 20–60] & PD patient 347 & Non-PD patient 213 \\ \hline  & & Web link of Dataset & \\ \hline Name & & Address & \\ \hline PD-DS-I & [[https://shorturl.at/tuzGJ](https://shorturl.at/tuzGJ)]([https://shorturl.at/tuzGJ](https://shorturl.at/tuzGJ)) (accessed on 26 December 2023) & \\ \hline PD-DS-II & [[https://shorturl.at/qtH15](https://shorturl.at/qtH15)]([https://shorturl.at/qtH15](https://shorturl.at/qtH15)) (accessed on 26 December 2023) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dataset Description. \[Sencitivity=\frac{TP}{TP+FN} \tag{10}\]
* **Specificity:** This metric measures the classifier's ability to accurately identify negative cases. It indicates the proportion of actual negatives (true negatives) that are correctly identified, reflecting the classifier's effectiveness in recognizing cases where the condition is absent.