Smart Detection for Healthy Heart

Abstract

This study presents a majority voting ensemble method aimed at predicting the likelihood Heart disease. likelihood, providing a reliable tool for early detection. Predictions are based on common, affordable medical tests available at local clinics. The primary aim is to enhance diagnostic accuracy and confidence by offering machine-assisted insights. Trained on real-world data from both healthy individuals and heart disease patients, the model ensures diverse and realistic predictions. Patient classification is determined by the majority vote of multiple machine learning models, each trained on the available medical data. This ensemble approach improves accuracy by combining the strengths of different models, reducing errors associated with relying on a single algorithm. Results show that the method achieves an impressive 90% accuracy, proving its effectiveness in delivering reliable heart disease predictions. The findings highlight its potential as a valuable tool for doctors and patients, providing timely and trustworthy guidance for heart disease detection.