Prediction Of Parkinson’s Disease And Severity Of The Disease Using Machine Learning And Deep Learning Algorithm

Project Synopsis & Overview

This project develops a Parkinson’s disease prediction system using machine learning and deep learning algorithms to analyze patient symptoms and clinical attributes. It incorporates dataset collection, preprocessing, exploratory data analysis, Principal Component Analysis (PCA), and classification. Random Forest, Support Vector Machine, Decision Tree, Naive Bayes, and Neural Network models are evaluated using accuracy scores and confusion matrices to compare predictive performance and support preliminary disease assessment.

Complete Technical Specifications