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Research Article

Performance Evaluation of Machine Learning Models for Cardiovascular Disease Prediction

Authors

  • Nuraini Usman Department of International Programmes, Jigawa State Polytechnic for Information and Communication Technology (JSPICT), Kazaure, Nigeria https://orcid.org/0009-0008-4287-7456

    message2nuraini@gmail.com

  • Babawuro Usman Department of Computer Science, Faculty of Computing and Mathematical Sciences (FACMS), Aliko Dangote University of Science and Technology (ADUSTECH), Wudil, Kano State, Nigeria
  • Zahriah Binti Sahri Department of Intelligence Computing, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malayisa

Abstract

Cardiovascular disease (CVD) is a deadly health issue that requires urgent attention due to the increasing global population. Using technological solutions, Machine Learning (ML) could be used to tackle health challenges in medical institutions. However, CVD diagnosis often involves multiple diagnostic procedures, leading to high medical errors. Using high-frequency and gamma rays in diagnostic devices exposes patients to high risks of other diseases. The acquisition of these devices is challenging in Low and Middle-Income Countries (LMICs), where patients are less privileged, and families lose their lives due to the inability to afford them. Healthcare institutions are utilizing the adoption of Artificial Intelligence (AI) to provide innovative solutions to these issues. In this research, the dataset was obtained from Kaggle. The Cross Industry Standard Process for Data Mining (CRISP-DM) framework was used. We selected five phases excluding the Deployment phase from the framework. Then, experiments conducted starting with exploratory data analysis, data cleansing, data visualization, transforming the dataset then splitting the CVD dataset, allocating 80% of the dataset to training and the remaining 20% as a testing dataset. We applied the different ML algorithms, where we achieved the best accuracy from Random Forest, Extra Tree Classifier, and Decision Tree all with 98.18% on the training dataset, for further experimental selection we tested the dataset where we obtained the following percentages: Random Forest 79.22%, Extra Tree Classifier 78.68% and Decision Tree 73.48%. Finally, the experiment featured the Random Forest algorithm as the best classifier compared to the rest as mentioned above, as it is more robust with the ability to handle non-linear and complex relationships making it more effective for CVD models.

Keywords:

Cardiovascular Disease CRISP-DM Framework Machine Learning Models Performance Evaluation Prediction Models

Article information

Journal

Journal of Computer, Software, and Program

Volume (Issue)

2(1), (2025)

Pages

39-48

Published

12-08-2025

How to Cite

Usman, N., Usman, B., & Sahri, Z. B. (2025). Performance Evaluation of Machine Learning Models for Cardiovascular Disease Prediction. Journal of Computer, Software, and Program, 2(1), 39-48. https://doi.org/10.69739/jcsp.v2i1.744

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