Early Prediction Model for Parkinsons 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), Logistic Regression, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and LightGBM to classify whether an individual exhibits signs of Parkinson’s Disease. Additionally, the project incorporates Explainable AI methods like SHAP and LIME to ensure that the model’s predictions are transparent and interpretable, which is essential for clinical acceptance. The developed system provides an intuitive interface where users can upload speech samples and receive predictions, offering a potential tool for early Parkinson’s Disease detection and aiding healthcare professionals in diagnosis.

Keywords: Parkinson’s Disease, Speech Data, Machine Learning, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, LightGBM, SHAP, LIME, Predictive Modeling.