Applying Healthcare Analytics to Diagnose and Predict Coronary Artery Disease Using Machine Learning Techniques

Worldwide, coronary artery disease (CAD) is one of the main causes of illness and mortality. Preventive measures can be aided by early detection and accurate CAD prediction, which can also considerably improve patient outcomes. Machine-learning techniques have become effective tools for CAD diagnosis and prediction in recent years. By utilizing vast datasets and sophisticated algorithms, these techniques can reveal hidden patterns and linkages in patient data. Predictive models can assess the likelihood of adverse events by integrating clinical data, laboratory findings, and imaging findings. The area of interest is risk prediction, in which machine learning algorithms examine a variety of patient data, including demographics, medical history, and clinical factors, to identify those who are very susceptible to developing CAD. An overview of machine learning's use in CAD identification and prediction is provided in this study.