Review on Cleveland Heart Disease Dataset using Machine Learning

Abstract

According to the World Health Organization (WHO), heart disease has been a foremost source of death worldwide for the past 15 years. Therefore Medical diagnosis is typically performed mostly by doctors due to their training and experience. In the field of medicine, computer-aided decision support systems are enormously significant. Therefore, it is necessary to develop prediction systems that give information of different categories to readers. According to the study, hybrid intelligent algorithms increase the heart disease prediction system’s accuracy. Hence, recognizing cardiovascular problems including heart attacks, coronary artery diseases, etc. by routine clinical data analysis is an important task; early identification of heart disease may save many lives. In this article, have reviewed various papers related to the Cleveland heart disease dataset that used one or more machine-learning algorithms to forecast congestive heart failure. In one of the above-mentioned papers, the result of utilizing Random Forest is almost 100%. To ensure that predictions made using machine learning algorithms produce accurate outcomes. Applying machine learning algorithms to heart disease treatment data can produce results that are just as accurate as those found in heart disease diagnosis.