Key result
HeartGuard machine-learning model predicts heart disease risk using structured clinical data.
Why the study?
Early risk prediction of heart disease is essential for timely intervention and improved patient outcomes due to its status as a leading cause of mortality worldwide.
HeartGuard provides a machine learning-based web application for real-time heart disease risk prediction using structured clinical data.
Should not yet change practice; leaves open prospective validation of ML risk prediction tools.
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.
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CH. Naresh (2026) studied Heart disease. HeartGuard was evaluated. HeartGuard, a machine learning-based framework using a Gradient Boosting Classifier, was developed to predict heart disease risk using structured clinical data.
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