## 3 Proposed Methodology
### Data Preprocessing
\[Specificity=\frac{TN}{TN+FN}\] (11)
* **Mathew's Correlation Coefficient (MCC):** This metric provides a balanced measure that evaluates the quality of binary classifications, taking into account both the positive and negative classes. MCC considers true and false positives and negatives, offering a comprehensive assessment of a classifier's performance, particularly in situations where class imbalances are present. It ranges from \(-1\) to \(+1\), where \(+1\) indicates a perfect prediction, 0 denotes random prediction, and \(-1\) represents complete disagreement between observation and prediction. \[MCC=\frac{(TP+TN+FP+FN)-(FP+FN)}{\sqrt{(TP+FN)(TP+FP)(TN+FN)(TN+FP)}}\] (12)
* **Area under the Receiver Operating Characteristics Curve (AUC)**: The Area under the Curve (AUC) of the Receiver Operating Characteristics (ROC) is a statistical metric for assessing the effectiveness of a binary classifier. It quantifies the likelihood that the model correctly ranks a randomly selected positive sample above a negative one. The superior performance of the model is indicated by a higher AUC-ROC score, with values nearing 1 denoting exceptional predictive precision. * **ANOVA**: ANOVA works by comparing the variance between groups (the variation between the group means) with the variance within groups (the variation within each group). If the between-group variance is significantly greater than the within-group variance, it suggests that there are statistically significant differences among the group means. * **Kappa Statistic:** This metric evaluates the agreement between the classification results and the true values. It compares the observed accuracy with what would be expected by chance. Mathematically, the Kappa statistic is calculated as follows: \[Kappa\ Statistics=\frac{2\times(TP\times TN-FN\times FP)}{(TP+FP)\times(FP+TN)+( TP+FN)\times(FN+TN)}\] (13)

### Baseline Techniques

Due to the novelty and multimodal nature of the proposed work, very little research has been found in this domain. However, three different baseline methods have been selected for the comparative analysis of the proposed work. * [PERSON] et al. [15]: An automated machine learning (ML) system was developed to analyze multimodal data from the Parkinson's Progression Markers Initiative (PPMI). Following the selection of the most effective algorithm, the complete PPMI dataset was utilized to fine-tune their chosen model. Subsequently, their model underwent validation using the PD Biomarker Program (PDBP) dataset. The preliminary results of our model demonstrated an area under the curve (AUC) of 89.72% in diagnosing PD. * [PERSON] et al. [41]: A deep learning framework developed to simultaneously analyze multimodal data, enabling concurrent predictions in three key areas: tremor severity, fine-motor skill impairment, and the likelihood or presence of Parkinson's disease (PD). This advanced framework integrates various data types to provide a comprehensive assessment of these interconnected health aspects. Building upon the aforementioned methods, our study also incorporates some of the single-model machine learning techniques for a comprehensive comparative analysis. The specifics of these additional models are detailed as follows:

* [PERSON] et al. [42]: Proposed an Artificial Intelligence-based approach for the effective diagnosis of PD using EEG signals. Their study centers on utilizing human bio-signals as a means for early detection of PD. * [PERSON] et al. [43]: Designed a biomarker that was developed using Topological Machine Learning applied to Resting-state Functional Magnetic Resonance Imaging (rs-fMRI). This work involved the creation of a spatial-temporal dimension reduction technique for rs-fMRI, specifically tailored for the automated diagnosis of PD. ### Results

The initial experiment was conducted to validate the efficacy of the proposed methodology in diagnosing PD. This was achieved by assessing key performance metrics such as specificity, accuracy, sensitivity, and F-score.