Prediction of diabetes, heart disease and Parkinson's disease using ML

Healthcare has witnessed significant evolution with the integration of machine learning (ML), enabling early disease detection and accurate predictions. This project presents a Multiple Disease Prediction System that utilizes RF, DT, and SVM to diagnose Diabetes, Heart Disease, and Parkinson’s Disease using Kaggle datasets. The system employs a data-driven approach, leveraging feature selection, preprocessing, and model optimization to enhance predictive performance. Among the algorithms evaluated, Random Forest achieved the highest accuracy across all three diseases, demonstrating its robustness in handling large datasets with complex patterns. The Decision Tree classifier was incorporated for its interpretability, providing insights into feature importance, while SVM was effective for high-dimensional medical data classification. The system follows a structured ML pipeline including data cleaning, normalization, hyperparameter tuning, and model evaluation to ensure optimal results.