Advanced Parkinson’s Prediction System Using Random Forest Algorithm and Feature Engineering

This system offers an enhanced Parkinson’s Disease Prediction Model utilizing machine learning for prompt diagnosis. By utilizing health and lifestyle information, it utilizes Random Forest for categorization, assessing its precision against K-Nearest Neighbors (KNN). The model goes through data preprocessing, feature engineering, and assessment to improve prediction accuracy. An interface built on Streamlit enables users to enter patient information and obtain real-time predictions along with confidence scores. Furthermore, analyzing feature importance assists in recognizing crucial contributing elements, aiding in medical decision-making. By prioritizing real-time forecasting, model interpretability, and scalability, this system supports early identification and improved disease management. Upcoming enhancements involve speech and walking assessment, incorporation of genetic information, and advanced deep learning frameworks for improved precision and usability . Key Words: Machine Learning, Parkinson’s Disease Prediction, Random Forest, K-Nearest Neighbors (KNN), Data Preprocessing, Feature Engineering, Model Evaluation, Confidence Score