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A Systematic Review of Machine Learning Models for Cardiac Disease Prediction

by Sunanda Budihal, Sheetalrani R. Kawale
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 63
Year of Publication: 2025
Authors: Sunanda Budihal, Sheetalrani R. Kawale
10.5120/ijca2025924439

Sunanda Budihal, Sheetalrani R. Kawale . A Systematic Review of Machine Learning Models for Cardiac Disease Prediction. International Journal of Computer Applications. 186, 63 ( Jan 2025), 27-33. DOI=10.5120/ijca2025924439

@article{ 10.5120/ijca2025924439,
author = { Sunanda Budihal, Sheetalrani R. Kawale },
title = { A Systematic Review of Machine Learning Models for Cardiac Disease Prediction },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 63 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number63/a-systematic-review-of-machine-learning-models-for-cardiac-disease-prediction/ },
doi = { 10.5120/ijca2025924439 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-31T17:28:30.734280+05:30
%A Sunanda Budihal
%A Sheetalrani R. Kawale
%T A Systematic Review of Machine Learning Models for Cardiac Disease Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 63
%P 27-33
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Heart disease is one of the leading causes of mortality worldwide, making its early detection and prediction crucial for saving lives. Machine learning (ML) algorithms have the potential to revolutionize the healthcare system by enhancing diagnostic accuracy and improving patient outcomes. This study reviews previous research that applied Deep Learning (DL) and ML techniques to predict heart disease. From the study it has seen that most of the work have used supervised ML algorithms, which includes Support Vector Machines (SVM), Gradient Boosting Classifier (GB), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR), have been employed on the UCI Machine Learning Repository (Heart) dataset to predict cardiac conditions. The accuracy of these algorithms varies, with studies reporting success rates between 88% and 95%. This review explores the factors influencing these outcomes, contributing to a better understanding of ML-based heart disease prediction models.

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Index Terms

Computer Science
Information Sciences

Keywords

Heart disease prediction machine learning deep learning supervised learning UCI heart dataset

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