A Comprehensive Study on Different Machine Learning Techniques to Predict Heart Disease
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Abstract
The heart is considered to be one of the most vital organs in the body. It contributes to the purification and circulation of blood throughout the body. Heart Diseases are responsible for the vast majority of fatalities around the world. Some symptoms, such as chest pain, a faster heartbeat, and difficulty breathing, have been documented. This data is reviewed regularly. In this review, a basic introduction related to the topic is first introduced. Furthermore, provide an overview of the healthcare industry. Then, an in-depth discussion of heart disease and the types of heart disease. After that, a summary of heart disease prediction, and different methods of heart disease prediction are also provided. Then, a short description of machine learning, also its different types, and how to use machine learning in the healthcare sector is discussed. And the most relevant classification techniques such as K-nearest neighbor, decision tree, support vector machine, neural network, Bayesian methods, regression, clustering, naïve Bayes classifier, artificial neural network, as well as random forest for heart disease is described in this paper. Then, a related work available on heart disease prediction is briefly elaborated. At last, concluded this paper with future research.
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