Parkinson’s Disease Prediction System in Machine Learning

Abstract

Around the globe, thousands of people worldwide are suffering by Parkinson’s Disease (PD), a central nervous system degenerative condition. Early detection and diagnosis of PD is crucial for successful treatment and management of the disease. In past few years, Machine learning (ML) algorithms has shown great potential in predicting PD based on various physiological and neurological markers. In this disease prediction system, a system is proposed using ML-based approach to predict the presence of PD in patients. The system employs various machine learning models, including Gradient Boosted Tree, random forest, and logistic regression, to identify key markers and patterns associated with the disease. Overall, this disease prediction system provides a valuable tool for early detection and diagnosis of PD, which can lead to better management and treatment of the disease. The proposed approach can also be extended to other neurological disorders, providing a general framework for disease prediction and diagnosis.