## 3 Proposed Methodology
### Data Preprocessing
For ANOVA, we ensured normality, homogeneity of variance, and independence of observations. Similarly, for the Kappa statistic, we confirmed the independence of raters, the appropriate scale of measurement, and the randomness of sampling. Figure 10: Comparisons with the baseline approaches. Figure 9: ROC for PD-DS-I and PD-DS-II. ## 5 Discussion

In this research, we have developed a multimodal machine learning approach for Parkinson's disease diagnosis using EEG and MRI data by utilizing FAWT, fMPRI, PCA, and LightGBM. The innovation of this study lies in the fusion of different modality datasets for discerning PD patients from healthy individuals. While many neuroimaging techniques are available for PD detection, they often require interpretation by experienced neurophysiologists and are prone to errors due to manual analysis, in addition to being costly. PD, a severe neurodegenerative disorder, diminishes dopamine levels in the brain, impacting the electrical potentials of neurons. These changes are closely associated with neurophysiological signals, specifically EEGs and MRI, which are recorded directly via scalp electrodes. Therefore, this work focuses on utilizing EEG signals as well as MRI for PD detection. EEG and MRI are regarded as crucial for the early and effective diagnosis of PD, offering low error rates, and are heavily relied upon by healthcare professionals in clinical decision-making. The integration of EEG and MRI data for [PERSON]'s disease diagnosis can offer more accurate and early detection. This approach may enable personalized treatment strategies, optimizing medication regimens, or timing of interventions. Additionally, it could provide insights into the underlying neural mechanisms of PD progression, potentially leading to the development of targeted therapies and improving patient outcomes. We further validated the performance of our framework by comparing it with other cutting-edge studies. Figure 10 and Table 6 present a comparison of accuracy with other contemporary works using dataset-I, dataset-II, and various state-of-the-art datasets, providing a comprehensive perspective of our analysis. Consequently, this discussion and comparative analysis strongly support that the proposed approach achieved better results as compared to the baselines. Despite its high detection rate in differentiating PD from HC using FAWT, fMPRI, PCA, and LightGBM, our methodology has certain limitations, which are as follows:

* The approach was formulated and evaluated using a small sample size from two datasets. To enhance the reliability of Parkinson's disease (PD) detection, it is recommended that the algorithms undergo testing with a more extensive sample base and across datasets from various regions. * The research was exclusively concentrated on a single nonlinear attribute, entropy, for diagnosing PD. Subsequent studies could investigate an array of nonlinear attributes and their synergies to derive more sophisticated features from EEG and MRI data for PD diagnosis. * One significant limitation of the study could stem from the variability in data quality and standardization across EEG and MRI acquisitions. Ensuring consistency in equipment, protocols, and participant factors across multiple sites is crucial but challenging. Additionally, limited sample sizes or homogeneity in participant demographics may restrict the generalizability of findings. Furthermore, integrating EEG and MRI data requires sophisticated analytical techniques, potentially introducing interpretation challenges and subjectivity, emphasizing the importance of transparent analysis methods and independent replication studies for validation. ## 6 Conclusions

[PERSON]'s disease is a neurodegenerative disorder characterized by symptoms that often overlap with other neurological conditions, highlighting the need for early and precise diagnosis to effectively manage treatment and improve a patient's quality of life.