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Artificial Intelligence in Health

ORIGINAL RESEARCH ARTICLE
Artificial intelligence model for prediction of

cardiovascular disease: An empirical study

Buhari Ugbede Umar , Lukman Adewale Ajao *, Eustace Mananyi Dogo ,
1
1
1
Falilat Jumoke Ajao , and Micheal Atama 1
2
1 Department of Computer Engineering, Federal University Technology, Minna, Niger State, Nigeria
2 Department of Computer Science, Kwara State University, Malete-Ilorin, Kwara State, Nigeria

Abstract
Cardiovascular disease (CVD) is a disease related to the heart and blood vessels.
Prediction of CVD is essential for early detection and diagnosis, which is however
compounded by the complex interplay between medical history, physical
examination outcomes, and imaging results. While the existing automated
systems are fraught with the usage of irrelevant and redundant attributes, artificial
intelligence (AI) helps in the identification of potential CVD populations by prediction
models. This work aims at developing an AI model for predicting CVD using different
classifications of machine learning techniques. The CVD dataset was obtained from
the UCI repository containing about 76 cardiac attributes for training in various
machine learning models, which include a hybrid of artificial neural network-
genetic algorithm (ANN-GA), artificial neural network, support vector machine
(SVM), K-means, K-nearest neighbor (KNN), and decision tree (DT). The performance
of the models was measured in terms of accuracy, means square error, sensitivity,
*Corresponding author: specificity, and precision. The results showed that the hybrid model of ANN-GA
Lukman Adewale Ajao performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN,
(ajao.wale@futminna.edu.ng)
and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed
Citation: Umar BU, Ajao LA, that the system performs better as the number of datasets increases in the database,
Dogo EM, et al., 2024, Artificial
intelligence model for prediction with a fewer selection of attributes using genetic algorithm for selection. Thus, the
of cardiovascular disease: An ANN-GA model is recommended for CVD prediction and diagnosis.
empirical study. Artif Intell Health,
1(1): 42-56.
https://doi.org/10.36922/aih.1746 Keywords: Artificial neural network; Cardiovascular disease; Genetic algorithm; Machine
Received: September 1, 2023 learning; Support vector machine
Accepted: November 14, 2023
Published Online: December 1, 2023
Copyright: © 2024 Author(s). 1. Introduction
This is an Open-Access article
distributed under the terms of the Cardiovascular disease (CVD) is a general term encompassing a broad category of
Creative Commons Attribution diseases affecting the components of the circulatory system, such as heart, blood vessels,
License, permitting distribution, and coronary artery . In coronary artery disease, a blockage caused by the buildup of
[1]
and reproduction in any medium,
provided the original work is fatty material in the coronary artery limits the pumping capacity and the blood flow
properly cited. of the heart, thereby result in heart or coronary failure. The CVDs present in several
Publisher’s Note: AccScience forms, including coronary heart conditions, cerebrovascular disease (stroke), high blood
Publishing remains neutral with pressure, peripheral artery disease, rheumatic heart disease, inborn cardiomyopathy
regard to jurisdictional claims in [2,3]
published maps and institutional disease, inflammatory heart condition, and coronary failure . Globally, the CVDs stand
affiliations. as the leading cause of morbidity and mortality and its prevalence is increasing, posing

Volume 1 Issue 1 (2024) 42 https://doi.org/10.36922/aih.1746