Parkinson's Disease Detection using Machine Learning

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects millions globally,characterized by motor symptoms such as tremors, bradykinesia, and rigidity, as well as non-motorsymptoms. Early diagnosis of PD is challenging due to its symptom overlap with other disorders, oftenresulting in delayed intervention. Recent deep learning (DL) advances offer promising approaches forenhancing early detection accuracy, leveraging large-scale datasets and advanced neural networks. Thispaper explores the application of DL techniques, including Random Forest(RF) and support vectorRegression(SVR) to analyze clinical data, gait patterns, and medical imaging for PD diagnosis. Wefocus on supervised learning models trained on various multimodal data sources to improve diagnosticprecision and identify potential biomarkers for early PD detection. Experimental results demonstrate thecapability of DL models to achieve high classification accuracy, contributing to reliable and accessiblediagnostic tools. Future work includes integrating additional data sources, refining algorithms tominimize computational costs, and implementing explainable AI for clearer clinical interpretations. Ourfindings underscore the transformative potential of deep learning in advancing Parkinson's diseasediagnostics, supporting timely and personalized medical intervention. Current research aims to addressthe challenges posed by late-stage diagnosis and limited treatment options by focusing on biomarkerdiscovery, genetic and molecular analysis, drug development, and computational modeling. Biomarkerresearch seeks to identify indicators in blood, cerebrospinal fluid, and neuroimaging data that can enableearly PD diagnosis before severe symptoms emerge. Genetic and molecular studies are advancing theunderstanding of hereditary and biochemical pathways involved in PD, informing personalizedtreatment strategies. Drug development efforts are exploring neuroprotective therapies to preserve orrestore affected brain cells. Additionally, machine learning approaches are being utilized to analyzecomplex datasets, identify patterns in disease progression, and customize patient care. Environmentaland lifestyle studies also play a role in examining external factors that may influence PD risk. Together,these approaches hold promise for transforming PD from a progressively debilitating condition into amanageable or preventable one, ultimately improving patient outcomes and reducing the societal impactof the disease.