Prediction of Parkinson’s Disease and Severity of Disease using Machine Learning and Deep Learning

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that affects motor functions, speech, and cognition. This project aims to develop an intelligent system for the detection of Parkinson’s Disease using both Machine Learning (ML) and Deep Learning (DL) techniques. We utilize publicly available datasets containing biomedical voice measurements and other biometric signals. The proposed approach involves data preprocessing, feature extraction, and model training using algorithms such as Support Vector Machines (SVM), Random Forest, and deep neural networks like Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN). Our results demonstrate that deep learning models, especially those tailored for time-series or audio data, offer improved accuracy compared to traditional ML models. This project highlights the potential of AI-based tools in assisting healthcare professionals with early and non-invasive diagnosis of Parkinson’s Disease.