INTERNATIONAL RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY

( Online- ISSN 2454 -3195 ) New DOI : 10.32804/RJSET

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HYBRID SOFT COMPUTATIONAL PREDICTION MODEL FOR HEART DISEASE RISK ANALYSIS

    2 Author(s):  M D ANANDA RAJ, DR.P.SURESH

Vol -  10, Issue- 1 ,         Page(s) : 6 - 19  (2020 ) DOI : https://doi.org/10.32804/RJSET

Abstract

Health care domain is improved with the help of artificial intelligence and machine learning techniques. Heart disease is one of the high risk diseases which affect human being due to their life style. Recent days, huge numbers of people perish due to this heart disease discomfort. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal source of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. The usage of machine learning techniques in the health care domain is to analyze extremely more massive health datasets and to identify the factor which causes heart disease from patient medical data. This procedure helps in earlier detection and finding of the deadly diseases. It gives the patients with a cost-effective and better quality of life. The focus of the research work is to develop and evaluate a soft computational prediction model for heart disease diagnosis with the help of machine learning techniques. The proposed model recognizes the typical issues of the cardiac problems and delivers a proper solution for medication at an earlier stage. Further, the proposed prediction model performance is evaluated with various classification performance measures and compared with existing prediction models.

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