Machine Learning for Early Detection of Heart Disease

Heart disease continues to be a leading cause of death globally, with early detection playing a critical role in improving outcomes and preventing severe complications. Traditional diagnostic methods, such as electrocardiograms (ECGs), echocardiograms, and stress tests, often detect conditions at later stages, when intervention becomes more challenging. This report explores the role of machine learning in early heart disease detection, highlighting various ML techniques such as supervised learning, deep learning, and unsupervised learning. These models can identify patterns and correlations between a wide range of risk factors, including medical history, lifestyle choices, genetic data, and biomarkers, enabling early intervention. However, challenges such as data quality, model interpretability, and generalization across diverse populations remain significant hurdles.