AccScience Publishing / AIH / Volume 1 / Issue 1 / DOI: 10.36922/aih.1746
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ORIGINAL RESEARCH ARTICLE

Artificial intelligence model for prediction of cardiovascular disease: An empirical study

Buhari Ugbede Umar1 Lukman Adewale Ajao1* Eustace Mananyi Dogo1 Falilat Jumoke Ajao2 Micheal Atama1
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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
AIH 2024, 1(1), 42–56; https://doi.org/10.36922/aih.1746
Submitted: 1 September 2023 | Accepted: 14 November 2023 | Published: 1 December 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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, specificity, and precision. The results showed that the hybrid model of ANN-GA performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN, and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed that the system performs better as the number of datasets increases in the database, with a fewer selection of attributes using genetic algorithm for selection. Thus, the ANN-GA model is recommended for CVD prediction and diagnosis.

Keywords
Artificial neural network
Cardiovascular disease
Genetic algorithm
Machine learning
Support vector machine
Funding
None.
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Conflict of interest
The authors declare that they have no competing interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Published by AccScience Publishing