Machine Learning Approaches for Detection and Diagnosis of Parkinson’s Disease

Description

This project attempts to evaluate the discriminative power of several predictors in the study to increase the efficiency of Parkinson’s disease detection through their symptoms. A number of classifiers including Support Vector Machine (SVM), Decision Tree, Neural Network, Random Forest classifier and Naïve Bayes (NB) are evaluated on a benchmark dataset obtained from Kaggle repository. It is observed that Neural Network achieved 100% accuracy.