Archives

AI-Powered Diagnostic Systems for Early Detection of Parkinson’s Disease

ABSTRACT

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that affects millions worldwide, with early detection being crucial for effective treatment and improved quality of life. Traditional diagnostic methods often rely on clinical assessments and observable motor symptoms, which may delay detection until significant neural damage has occurred. This research explores the design and implementation of AI-powered diagnostic systems capable of identifying early markers of PD using advanced machine learning (ML) and deep learning (DL) models. Leveraging multimodal data sources—including voice analysis, handwriting patterns, gait monitoring, and neuroimaging—the proposed hybrid framework integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and feature fusion techniques to enhance predictive accuracy. Using datasets such as the Parkinson’s Progression Markers Initiative (PPMI) and real-world clinical trial data, the system demonstrates high precision, recall, and F1-scores, outperforming conventional diagnostic approaches. The study also discusses hyperparameter optimization, model interpretability, and integration into healthcare workflows, emphasizing the potential of AI systems to revolutionize early PD detection, facilitate timely interventions, and reduce the overall burden of the disease.

FULL TEXT