Machine Learning Method for Analyzing and Predicting Cardiovascular Disease

Heart Disease is among the leading causes of mortality globally. Heart disease is responsible for delivering plasma to every portion of the body. Frequent causes of cardiac arrest are ischemic heart disease (CAD) and congestive heart failure (CHF). Traditional medical techniques (such as angiography) have higher costs and significant health risks and are used to diagnose heart disease. Therefore, scientists have developed a number of robotic detection methods employing ML algorithms and knowledge discovery techniques. ML-based computer-aided diagnostic techniques make detecting cardiovascular disease simple, efficient, and trustworthy. In the past, multiple machine learning, data gathering, and information sources have been utilized. In numerous past evaluations, several research articles devoted to a specified data format have been released. Likewise, the purpose of this work is to conduct a comprehensive analysis of computerized diagnostics for cardiovascular disease prognosis using multiple techniques.