BeatWell: A Machine Learning Approach for Accurate Heart Disease Risk Prediction

Cardiovascular diseases are a leading cause of global mortality, often detected late due to the limitations of traditional diagnostic methods like manual ECG interpretation and blood tests, which are time-consuming and error-prone. This study presents BeatWell, an advanced machine learning-based system designed to enhance early detection of heart disease by analyzing clinical parameters, including age, cholesterol, blood pressure, and ECG results. Leveraging ensemble models such as Random Forests and Gradient Boosting, BeatWell delivers real-time risk assessments to support timely clinical decisions. Model interpretability is achieved using LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to highlight key risk factors like glucose and BMI. This research leverages a well-curated heart disease dataset, applies meticulous data preparation, and employs multiple classification techniques for model development. BeatWell demonstrates strong potential to revolutionize heart disease prediction, offering a scalable, data-driven approach to improve patient outcomes and optimize healthcare efficiency. Techniques including XGBoost, Random Forest, Decision Trees, K-Nearest Neighbors (KNN), and Logistic Regression are utilized to compare against the BeatWell model and evaluate the system’s performance during training and testing.