Paper Title : HEART DISEASE IDENTIFICATION USING MACHINE LEARNING METHODOLOGIES

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6463324

Authors: Ms. N. Zahira Jahan, M.C.A., M.Phil., Mr. S. Vigneshkumar

         



MLA Style: HEART DISEASE IDENTIFICATION USING MACHINE LEARNING METHODOLOGIES "Ms. N. Zahira Jahan, M.C.A., M.Phil., Mr. S. Vigneshkumar" Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: HEART DISEASE IDENTIFICATION USING MACHINE LEARNING METHODOLOGIES "Ms. N. Zahira Jahan, M.C.A., M.Phil., Mr. S. Vigneshkumar" Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
In day-to-day life, there are numerous factors which affect a mortal heart. Numerous problems are being at a rapid-fire pace and new heart conditions are fleetly being identified. In moment’s world of stress Heart, being an essential organ in a mortal body which pumps blood through the body for the blood rotation is essential and its health is to be conserved for a healthy living. The main provocation of doing this design is to present a heart complaint vaticination model for the vaticination of circumstance of heart complaint. Further, this exploration work is aimed towards relating the stylish bracket algorithm for relating the possibility of heart complaint in a case. The identification of the possibility of heart complaint in a person is complicated task for medical interpreters because it requires times of experience and violent medical tests to be conducted. In this work, three data mining bracket algorithms like KNN bracket, SVM bracket, Naïve Bayes and Random Forest are addressed and used to develop a vaticination system in order to dissect and prognosticate the possibility of heart complaint. The main idea of this significant exploration work is to identify the algorithms suitable for providing maximum accuracy when classification of normal and abnormal person is carried out. Therefore prevention of the loss of lives at an earlier stage is being possible. It is sure that Random Forest algorithm performs better when compared to other algorithms for heart complaint prediction. The design is designed using R Language3.4.4 with R Studio.

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Keywords
— Data mining, Prediction model Classification algorithms, Feature selection, Heart disease prediction