A survey on Intelligent Data Mining Techniques used in Heart Disease Prediction

Abstract

Imminent need of turning huge amount of available health data into useful information and knowledge attracts data mining techniques in medical diagnosis process. Data mining is a procedure of distinguishing and extracting valuable data and setting up connection between attributes in substantial datasets. Existing heart disease prediction models use one or multiple data mining techniques. This paper surveys heart disease prediction systems systematically wherein techniques are compiled, tabulated and analyzed based on hybrid techniques categorization. In this paper, the techniques are classified into two main categories: Discrete and Integrated, which are further classified as supervised, unsupervised, hybrid and miscellaneous. It is revealed from this survey, even though usage of one data mining technique performs well, hybrid data mining techniques yield promising outcomes in the determination of coronary illness.