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Big Data Analytics in Healthcare: Machine Learning-based Cardiac Disease Prediction in West Africa

by Ame´de´e W. Dera, Ferdinand T. Guinko
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 41
Year of Publication: 2025
Authors: Ame´de´e W. Dera, Ferdinand T. Guinko
10.5120/ijca2025925718

Ame´de´e W. Dera, Ferdinand T. Guinko . Big Data Analytics in Healthcare: Machine Learning-based Cardiac Disease Prediction in West Africa. International Journal of Computer Applications. 187, 41 ( Sep 2025), 6-12. DOI=10.5120/ijca2025925718

@article{ 10.5120/ijca2025925718,
author = { Ame´de´e W. Dera, Ferdinand T. Guinko },
title = { Big Data Analytics in Healthcare: Machine Learning-based Cardiac Disease Prediction in West Africa },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2025 },
volume = { 187 },
number = { 41 },
month = { Sep },
year = { 2025 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number41/big-data-analytics-in-healthcare-machine-learning-based-cardiac-disease-prediction-in-west-africa/ },
doi = { 10.5120/ijca2025925718 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-09-23T00:37:01.473079+05:30
%A Ame´de´e W. Dera
%A Ferdinand T. Guinko
%T Big Data Analytics in Healthcare: Machine Learning-based Cardiac Disease Prediction in West Africa
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 41
%P 6-12
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper investigates the application of machine learning for cardiac disease prediction in resource-constrained healthcare settings. This study conducts an empirical study evaluating four classification algorithms (Support Vector Machine, Random Forest, Logistic Regression, Decision Tree) on a real-world dataset. The results demonstrate that SVM achieves the highest accuracy (91%) in identifying high-risk patients, highlighting its potential for clinical decision support. The study provides a detailed comparative analysis of model performance, discusses computational feasibility, and outlines practical deployment considerations. These findings contribute to the advancement of machine learning applications in African healthcare systems.

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Index Terms

Computer Science
Information Sciences

Keywords

Big Data Analytics Data-driven healthcare Data analytics in healthcare Machine Learning in Healthcare Disease Prediction