A Review Paper on the Comparative Study of Intelligent Heart Disease Prediction System

Abstract

In order to remove useful information, large amounts of medical data require intelligent equipment. Techniques have been implemented in a number of different areas, including bioinformatics, business, industry, computer vision. Many researchers have been done with the help of such techniques. There is a lack of effective analysis tools to find hidden relationships and trends in medical data from clinical records. Heart disease is considered the leading cause of death worldwide in the last 15 years. Medical data is still rich in information but knowledge is poor. Researchers have used several statistical analyses and various health care techniques or tools to improve the diagnosis accuracy in the medical healthcare service. In this paper, it was majorly discussed all the research work being carried out using the data mining techniques to enhance heart disease diagnosis and prediction including decision trees, Naive Bayes classifiers, K-nearest neighbor classification (KNN), support vector machine (SVM), decision tree and PCA. Results show that SVM and knn perform positively high to predict the presence of coronary heart diseases (CHD). The use of a decision tree is considered as the best-recommended classifier to diagnose cardiovascular disease (CVD). Still, the performance of data mining techniques to detect coronary arteries diseases (CAD) is not encouraging (between 70—80%) and further improvements should be pursued.