###### Abstract  
Parkinson's disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient's quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model's efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages. Parkinson's disease; feature extraction; EEG; MRI; FAWT; LightGBM +
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* **Tremor at Rest:** This symptom involves involuntary shaking, typically noticeable when the body is at rest, which diminishes during purposeful movement [5]. It often starts on one side of the body, with the hand being a common initial site. * **Rigidity:** This is characterized by an involuntary increase in muscle tone, leading to stiffness in the muscles [6]. * **Postural Instability:** Individuals with PD often have a compromised sense of balance, which can lead to frequent falls [7]. To counter this instability, many patients adopt a stooped posture by lowering their center of gravity. The World Health Organization indicates that approximately 10 million people worldwide suffer from PD [8]. Early diagnosis is crucial, yet many cases remain undetected until the disease progresses to an advanced, incurable stage [9]. In 2015, about 6.2 million individuals were affected by PD, leading to 117,400 deaths [10]. A significant challenge in combating PD is the high cost and limited accuracy of current diagnostic tests [11; 12]. This situation underscores the critical need for an affordable, effective, and precise diagnostic tool for early-stage PD. Such a development could enable timely treatment, offering a chance to cure the disease before it progresses beyond treatment. Diagnosing PD is challenging due to the lack of definitive clinical tests like blood work, often leading to late detection, particularly in individuals younger than 60 [13]. Moreover, other health conditions and medications can also mimic Parkinson's disease symptoms, so accurate diagnosis by a physician is crucial [14; 15; 16]. The variability of symptoms among individuals means that no single test can conclusively diagnose Parkinson's disease (PD). During a physical exam, potential PD patients may be asked to perform specific movements to assist in diagnosing. For instance, when individuals with Parkinson's are instructed to touch their noses with their hands, their tremor often reduces or vanishes.