Abstract: According to the recent report published by WHO, Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. CVDs are a group of disorders of the heart and blood vessels and includes rheumatic heart disease, cerebrovascular disease, coronary heart disease and other conditions. The basic cause of CVD death is due to heart attacks and strokes and one third of these deaths occur prematurely in folks under 70 years of age. With rapid increase in population, pollution and frequently changing lifestyle of a human being, it becomes a challenge to diagnose a disease and provide the relevant ministration at the right time. With the help of advancements in the technological tools and techniques, machine learning plays a vital role in training and testing the abundant data in the medical field and takes less time in predicting the same with foremost correct and reliable formulas. In this paper we have surveyed the various research papers published in this domain in the recent years and formulated a table which includes various techniques and their corresponding algorithms used with their level accuracy, pros and limitations and also studied the future scope so as to propose a model in the near future which predicts the heart disease with high degree of accuracy and results in robust way of saving the lives at large.
Keywords: KNN, SVM, DECISION TREE, LOGISTIC REGRESSION
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DOI:
10.17148/IJARCCE.2023.124202
