Machine Learning-Based Cardiac Disease Prediction Survey Paper

Abstract

An overview of recent studies employing machine learning approaches to predict cardiac disease is given in this survey paper. One of the main causes of death in the world is cardiac disease, and effective treatment and prevention depend on early detection. Machine learning algorithms have been routinely used to evaluate sizable datasets, pinpoint probable cardiac disease risk factors, and forecast future cardiac disease propensity.In this research, we discuss different machine learning methods, including decision trees, support vector machines, artificial neural networks, and random forests, that are used to predict cardiac disease. In order to enhance the performance of these models, several feature selection and dimensionality reduction strategies are also examined.Finally, we outline potential future research avenues in this field and examine the difficulties and restrictions of utilizing machine learning to predict cardiac disease.