Early Prediction Of Parkinson's Disease Using Machine Learning

Parkinson's Disease (PD) is a neurodegenerative disorder that affects movement control, leading to symptoms such as tremors, stiffness, and bradykinesia. Early detection plays a crucial role in managing the disease effectively. Traditional diagnostic methods often require medical imaging or clinical assessments, which can be time-consuming and expensive. This project explores the use of machine learning models to predict Parkinson's Disease from speech data, a non-invasive and accessible source. By analyzing features such as pitch, tone, and rhythm from speech samples, the project leverages machine learning algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (KNN) to classify whether an individual exhibits signs of Parkinson's Disease. Addi-tionally, the project incorporates. The developed system provides an intuitive interface where users can upload speech samples and receive predictions, offering a potential tool for early Par-kinson's Disease detection and aiding healthcare professionals in diagnosis.