Predicting Coronary Artery Disease using Explainable Machine Learning

Coronary artery disease (CAD) has been one of the leading causes of death worldwide; it is a result of narrowing or blocking of the coronary arteries due to the buildup of plaque, which reduces the flow of blood into the heart. Accurate and early diagnosis of CAD is especially important for proper treatment in pursuit of better outcomes for patients. However, traditional diagnostic methods, such as angiography, are invasive, time-consuming, and resource-intensive, which calls for non-invasive alternatives. This study applies machine learning in the analysis of the dataset Z-Alizadeh Sani, which encompasses 303 patient records grouped into 53 features like demographic, symptom and examination, ECG, laboratory, and echo data. Machine learning can predict with greater accuracy, speed, and efficiency CAD. It could also reveal important patterns and features, supporting clinicians in decision-making and personalized care. The presented research has shown the potential of machine learning in revolutionizing CAD diagnosis, thereby providing non-invasive, cost-effective, and reliable diagnostic tools. That can improve outcomes in health care but also take some burden off the healthcare systems.