An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care System

Abstract

Health care Management System (HMS) is a key to successful management of any health care industry. Health care management systems have so many research dimensions such as identifying disease and diagnostic, drug discovery manufacturing, Bioinformatics’ problem, personalized treatments, Patient image analysis and so on. Heart Disease Prediction (HDP) is a process of identifying heart disease in advance and recognizes patient health condition by applying techniques on patient heart related symptoms. Now a day’s the problem of identifying heart diseases is solved by machine learning techniques. In this paper we construct a heart disease prediction method using combined feature selection and classification machine learning techniques. According to the existing study the one of the main difficult in heart disease prediction system is that the available data in open sources are not properly recorded the necessary characteristics and there is some lagging in finding the useful features from the available features. The process of removing inappropriate features from an available feature set while preserving sufficient classification accuracy is known as feature selection. A methodology is proposed in this paper that consists of two phases: Phase one employs two broad categories of feature selection techniques to identify the efficient feature sets and it is given to the input of our second phase such as classification. In this work we will concentrate on filter-based method for feature selection such as Chi-square, Fast Correlation Based Filter (FCBF), Gini Index (GI), RelifeF, and wrapper-based method for feature selection such as Backward Feature Elimination (BFE), Exhaustive Feature Selection (EFS), Forward Feature Selection (FFS), and Recursive Feature Elimination (RFE). The UCI heart disease data set is used to evaluate the output in this study. Finally, the proposed system's performance is validated by various experiments setups.