A Comparative Performance Study of Machine Learning Techniques for  Heart Disease Prediction

P. Srivyshnavi, M Darshan Teja  ,P Shankaraiah , Sreenivasulu T

A Comparative Performance Study of Machine Learning Techniques for Heart Disease Prediction

Authors

P. Srivyshnavi, M Darshan Teja ,P Shankaraiah , Sreenivasulu T

Keywords:

Heart Disease Prediction, Machine Learning, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, XGBoost, Healthcare Analytics

Abstract

Heart disease is a leading cause of death and disease globally and places a burden on health systemsand society. Early detection of risk of heart disease improves the patients’ outcome and minimizesdeath. Healthcare data is growing at a breakneck pace. Machine learning is used for disease prediction, clinical decision support. This paper compares

References

Alizadehsani R, Zangooei MH, Hosseini MJ, Habibi J, Khosravi A, Roshanzamir M, Khozeimeh F, Sarrafzadegan N, Nahavandi S. Coronary artery disease detection using computational intelligence methods. Knowledge-Based Systems. 2016 Oct 1;109:187-97.

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Published

2024-02-20

How to Cite

P. Srivyshnavi, M Darshan Teja ,P Shankaraiah , Sreenivasulu T. (2024). A Comparative Performance Study of Machine Learning Techniques for Heart Disease Prediction. Journal of Computational Analysis and Applications (JoCAAA), 32(2), 799–813. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5526