Why the study?
Heart disease detection is challenging and treatments are expensive, making early prediction valuable for enabling preventive action.
This survey highlights the potential of various supervised machine learning algorithms for the early prediction and detection of heart disease.
ML algorithms may aid early heart disease prediction; leaves open need for prospective validation before clinical use.
Predicting and detection of heart disease has always been a critical and challenging task for healthcare practitioners. Hospitals and other clinics are offering expensive therapies and operations to treat heart diseases. So, predicting heart disease at the early stages will be useful to the people around the world so that they will take necessary actions before getting severe. Heart disease is a significant problem in recent times; the main reason for this disease is the intake of alcohol, tobacco, and lack of physical exercise. Over the years, machine learning shows effective results in making decisions and predictions from the broad set of data produced by the health care industry. Some of the supervised machine learning techniques used in this prediction of heart disease are artificial neural network (ANN), decision tree (DT), random forest (RF), support vector machine (SVM), naïve Bayes) (NB) and k-nearest neighbour algorithm. Furthermore, the performances of these algorithms are summarized.
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Katarya et al. (2020) studied this question.
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