PREVENTION OF PARKINSON’S DISEASE USING ARTIFICIAL NUERAL NETWORKS

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects the central nervous system, leading to motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor symptoms including cognitive decline. Early detection and intervention are crucial to slow disease progression and improve the quality of life for patients. Traditional diagnostic methods often rely on subjective clinical assessments, which may delay timely intervention. This research proposes a prevention and early detection framework for Parkinson’s disease using Artificial Neural Networks (ANNs). The system utilizes patient data, including demographic information, medical history, sensor-based motion analysis, and vocal features, to train an ANN model capable of predicting the risk of PD onset. By identifying high-risk individuals early, the model facilitates proactive preventive measures and lifestyle adjustments. Experimental results demonstrate that the ANN model achieves high accuracy and reliability in predicting Parkinson’s disease risk, outperforming conventional statistical approaches. This approach highlights the potential of AI-driven predictive models in healthcare, offering a cost-effective, noninvasive, and efficient solution for early prevention and management of Parkinson’s disease.