Measuring the Heart Attack possibility Using Different Types of Machine Learning Algorithms

Authors

  • Gajula Yashasvi Student, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India
  • Jakkali Sampath Kumar Student, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India
  • Jebin Sathish Rajan Student, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India
  • M. Revathi Assistant Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India

DOI:

https://doi.org/10.5281/zenodo.10968502

Keywords:

Machine algorithm, Heart attack, Logistic Regression, SVM

Abstract

Today, mortality from cardiovascular sicknesses has become a critical trouble. Every minute a person dies of coronary heart disease. This is with male and lady classes; this ratio varies with the aid of application. In the United States of America, this technique is also taken into consideration for the elderly. It is not they notice that heart damage does not occur in different age organizations Diseases this problem begins at an early age and its reason can be predicted. And sicknesses are a completely severe trouble these days. Here in this newsletter, we've got various techniques (logistic regression and SVM) used for forecasting are mentioned Heart sicknesses.

Downloads

Download data is not yet available.

Downloads

Published

13-04-2024

Issue

Vol. 7 No. 4 (2024)

Section

Articles

License

Copyright (c) 2024 Gajula Yashasvi, Jakkali Sampath Kumar, Jebin Sathish Rajan, M. Revathi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
G. Yashasvi, J. S. Kumar, J. S. Rajan, and M. Revathi, “Measuring the Heart Attack possibility Using Different Types of Machine Learning Algorithms”, IJRESM, vol. 7, no. 4, pp. 68–72, Apr. 2024, doi: 10.5281/zenodo.10968502.