Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Ogunpola, Adedayo and Saeed, Faisal and Basurra, Shadi and Albarrak, Abdullah M. and Qasem, Sultan Noman (2024) Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics, 14 (2). p. 144. ISSN 2075-4418

Preview

Text diagnostics-14-00144-v2.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB)

Abstract

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.

Item Type:	Article
Identification Number:	10.3390/diagnostics14020144
Dates:	Date Event 25 December 2023 Accepted 8 January 2024 Published Online
Uncontrolled Keywords:	cardiovascular diseases, deep learning, disease detection, heart diseases, machine learning, ensemble learning, XGBoost
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	15 Feb 2024 13:48
Last Modified:	15 Feb 2024 13:48
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15224

Actions (login required)

View Item