Parkinson’s Disease Detection Using Machine Learning

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting movement and motor control. Early detection is crucial for effective treatment and management. This paper presents a machine learning-based approach to detect Parkinson’s disease using speech and biomedical data. The proposed model utilizes various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Deep Learning techniques, to classify PD and non-PD subjects. The model is trained on a publicly available dataset and achieves significant accuracy in classification. Multi-modal analysis enhances diagnostic accuracy, offering a non-invasive, cost-effective solution. Future work will focus on real-time monitoring, expanding datasets, integrating wearable technology, and improving model interpretability for clinical applications.