HeartGuard: A Machine Learning-Based Framework for Accurate Heart Disease Prediction and Real-Time Clinical Decision Support

Abstract

Heart disease remains a leading cause of mortality worldwide, making early risk prediction essential for timely intervention and improved patient outcomes. This paper presents HeartGuard, a machine learning-based heart disease prediction system that analyzes structured clinical data, including age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, ECG results, maximum heart rate, and related attributes. The proposed framework performs data preprocessing, feature handling, model training, and prediction using a Gradient Boost-ing Classifier trained on the Cleveland heart disease dataset. The trained model is integrated into a Django-based web application that enables users to enter health parameters and receive instant risk predictions through a simple interface.