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.
Health Care Management System, Heart Disease Prediction, Machine Learning Techniques, Feature Selection
Techniques, Classification, Filter FS, Wrapper FS.
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Author(s) thanks to Dr.Nithyanandam S for this research completion and support.
No funding was received to assist with the preparation of this manuscript.
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
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No data available for above study.
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Department of Computer Science and Engineering, Ponnaiyah Ramajayam Institute of Science and Technology (PRIST) Deemed to be University, Thanjavur, India.
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Cite this article
Keerthika N and Nithyanandam S, “An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care System”, Journal of Machine and Computing, vol.3, no.4, pp. 566-581, October 2023. doi: 10.53759/7669/jmc202303048.