Abstract
Cardiovascular Diseases (CVDs) are one of the most common health problems nowadays. Early diagnosis of heart disease is a significant concern for health professionals in medical centers. An incorrect forecast is more likely to have negative effects, such as disability or even death. Our research is motivated by the desire to predict cardiovascular diseases based on data mining that can be valuable to medical centers. Various data mining approaches are used for the early detection of cardiac diseases. This paper examines several research publications that work on various heart diseases. We compare and contrast several machine learning methods, such as KNN, ANN, Decision Tree, SVM, and Random Forest. We looked at 918 observations with several features related to heart disease. A comparative study with age and sex is established to predict cardiac disease using the decision tree approach. Our dataset contains 11 features that are used to forecast possible heart disease. One of the attributes indicates that the age factor has the most significant impact on heart disease. According to our findings, heart attacks cause four out of every five CVD deaths, with one-third of these deaths occurring suddenly in those under 70.
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