Myocardial Infarction Risk Estimation Using Machine Learning

Abstract

This project focuses on applying machine learning techniques to predict heart disease, with the ultimate goal of improving diagnosis and treatment through accurate and timely detection. By leveraging a diverse dataset that encompasses a range of clinical and demographic attributes, we build predictive models using various machine learning algorithms. Key features considered in our analysis include age, gender, blood pressure, cholesterol levels, and ECG results. Through rigorous testing, our models demonstrate strong performance metrics, including high accuracy, precision, recall, and F1-score. By contributing to the early detection of heart disease, this project has the potential to make a significant impact in the medical field, enabling healthcare professionals to intervene earlier and improve patient outcomes.