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Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis

Aparna Datta1 , Sreeja Ghosh2

Section:Research Paper, Product Type: Journal Paper
Volume-11 , Issue-01 , Page no. 76-80, Nov-2023

Online published on Nov 30, 2023

Copyright © Aparna Datta, Sreeja Ghosh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Aparna Datta, Sreeja Ghosh, “Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.76-80, 2023.

MLA Style Citation: Aparna Datta, Sreeja Ghosh "Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis." International Journal of Computer Sciences and Engineering 11.01 (2023): 76-80.

APA Style Citation: Aparna Datta, Sreeja Ghosh, (2023). Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis. International Journal of Computer Sciences and Engineering, 11(01), 76-80.

BibTex Style Citation:
@article{Datta_2023,
author = {Aparna Datta, Sreeja Ghosh},
title = {Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2023},
volume = {11},
Issue = {01},
month = {11},
year = {2023},
issn = {2347-2693},
pages = {76-80},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1415},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1415
TI - Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis
T2 - International Journal of Computer Sciences and Engineering
AU - Aparna Datta, Sreeja Ghosh
PY - 2023
DA - 2023/11/30
PB - IJCSE, Indore, INDIA
SP - 76-80
IS - 01
VL - 11
SN - 2347-2693
ER -

           

Abstract

Heart disease has been a serious threat to mankind. According to research 7 out of 10 people die due to heart failure. In this paper, we have proposed a framework using which we can determine if a person has heart ailments or not. We have used various ML classification algorithms such as Logistic Regression, SVM, Random Forest, Decision tree, KNN, MLP, and Neural Network to determine the existence of heart disease. The best result has been obtained by Random Forest. Timely detection of a disease can save many people’s lives, thereby controlling the mortality rate to some extent.

Key-Words / Index Term

Cardiovascular disease, KNN, SVM, Neural Network, Random Forest, Decision Tree, MLP, Logistic regression

References

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