Abstract
Cardiovascular Diseases (CVDs) are the leading cause of death worldwide, according to the World Health Organization: more people die each year from CVDs than from any other cause. CVDs claimed the lives of 17.9 million people worldwide in 2016, accounting for 31% of all deaths. Heart attacks and strokes account for 85 percent of these deaths. The proposed work is about the application of ML (Machine Learning) techniques for the classification and prediction of heart diseases (HD). The scope of the present study includes investigations of main determinants to remove irrelevant and redundant features using the feature selection technique, compare different Machine Learning classification algorithms on the heart disease data set and to identify better performance-based classification technique for heart disease classification.
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