A Comparative Study of Machine Learning Models for Heart Disease Prediction

Authors

  • Sunanda Budihal Department of Computer Science, Shah Sogamal Peeraji Oswal Government First Grade College, Muddebhihal, Vijayapura, Karnataka, India.
  • Sheetalrani Rukmaji Kawale Department of Computer Science, Karnataka State Akkamahadevi Women University, Vijayapura, Karnataka, India.
  • Aparna Atul Junnarkar Department of Information Technology, Vishwakarma Institute of Information Technology (VIIT), Pune, Maharashtra, India.
  • H. Faritha Begam Seethalakshmi Achi College for Women, Pallathur 630 107, Tamilnadu, India.
  • Girish M. Department of Computer Science and Engineering, Gopalan College of Engineering and Management, Bangalore, Karnataka, India.

DOI:

https://doi.org/10.9734/bpi/acst/v2/6348C

Keywords:

Heart disease prediction, machine learning models, comparative study, logistic regression, decision trees, random forest, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN)

Abstract

Heart disease continues to be a major global public health issue, contributing to innumerable deaths and disabilities. Effective preventive interventions and individualized treatment programs depend on timely and precise risk prediction of heart disease. Significant advancements in the field of cardiac disease prediction have been made thanks to the development of machine learning techniques. This book chapter offers a thorough analysis of the strengths, flaws, and overall effectiveness of the various machine learning models used for heart disease prediction.

Published

2023-09-20

How to Cite

Sunanda Budihal, Sheetalrani Rukmaji Kawale, Aparna Atul Junnarkar, H. Faritha Begam, & Girish M. (2023). A Comparative Study of Machine Learning Models for Heart Disease Prediction. Advances and Challenges in Science and Technology Vol. 2, 59–71. https://doi.org/10.9734/bpi/acst/v2/6348C

Issue

Advances and Challenges in Science and Technology Vol. 2

Section

Chapters