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Enhanced Heart Disease Prediction using Ensemble of Machine Learning Models

by Rajneesh Shrivastava, Chandra Shekhar Gautam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 104
Year of Publication: 2026
Authors: Rajneesh Shrivastava, Chandra Shekhar Gautam
10.5120/ijca691b519f7327

Rajneesh Shrivastava, Chandra Shekhar Gautam . Enhanced Heart Disease Prediction using Ensemble of Machine Learning Models. International Journal of Computer Applications. 187, 104 ( May 2026), 40-46. DOI=10.5120/ijca691b519f7327

@article{ 10.5120/ijca691b519f7327,
author = { Rajneesh Shrivastava, Chandra Shekhar Gautam },
title = { Enhanced Heart Disease Prediction using Ensemble of Machine Learning Models },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 104 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number104/enhanced-heart-disease-prediction-using-ensemble-of-machine-learning-models/ },
doi = { 10.5120/ijca691b519f7327 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-17T02:29:17.038345+05:30
%A Rajneesh Shrivastava
%A Chandra Shekhar Gautam
%T Enhanced Heart Disease Prediction using Ensemble of Machine Learning Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 104
%P 40-46
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Early identification is essential for efficient treatment of heart disease, which continues to rank among the leading causes of mortality worldwide. This article proposes an ensemble-based machine learning approach for cardiac disease prediction using the Cleveland dataset. Unlike prior research that focused on only two algorithms, this study integrates six supervised learning models—K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes—into a single ensemble system. GridSearchCV-based hyperparameter optimization is used to optimize model accuracy. The ensemble model outperformed the individual models in terms of accuracy, with a prediction accuracy of over 90%. This approach supports computerized diagnosis and early medical intervention.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Heart Disease Ensemble Learning Machine Learning KNN SVM Logistic Regression Random Forest Naive Bayes Cleveland Dataset

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