Parkinson’s Disease Prediction

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects motor function. Early detection of PD is crucial for effective treatment and management of the disease. In this study, we propose a machine learning-based approach for the prediction of PD. Our method uses a combination of demographic information and clinical measurements to train a model for PD prediction. We evaluate the performance of our model using a dataset of patients diagnosed with PD and healthy controls. Our results show that the proposed model can achieve high accuracy in predicting PD, with an AUC of 0.89.