AI Cardiologist: Personalized Heart Disease Risk Prediction System

The increasing prevalence of cardiovascular diseases has intensified the demand for intelligent healthcare systems capable of early risk detection and prediction. This project presents a machine learning-based predictive model designed to assess the likelihood of heart attacks using clinical and physiological data. By analyzing features such as age, cholesterol levels, resting blood pressure, and chest pain types, the system offers a data-driven approach to proactive healthcare. The dataset undergoes rigorous preprocessing and Exploratory Data Analysis (EDA) to reveal underlying patterns and correlations among critical risk factors. Multiple supervised learning algorithms—including Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Neural Networks—are trained and evaluated to identify the most effective model. Feature importance is interpreted using Explainable AI (XAI) techniques, such as SHAP and LIME, ensuring transparency in decision-making and fostering trust among medical practitioners. Model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The best-performing model demonstrates strong generalization and predictive capabilities, making it suitable for clinical decision support. The system significantly aids in early intervention strategies, reducing the risk of fatal outcomes through timely medical attention. In conclusion, the AI Cardiologist system leverages machine learning and explainability to offer a reliable and interpretable solution for heart attack risk prediction, contributing to smarter, preventive healthcare.