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Article

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

1
DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
2
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(2), 144; https://doi.org/10.3390/diagnostics14020144
Submission received: 27 November 2023 / Revised: 21 December 2023 / Accepted: 25 December 2023 / Published: 8 January 2024
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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.
Keywords: cardiovascular diseases; deep learning; disease detection; heart diseases; machine learning; ensemble learning; XGBoost cardiovascular diseases; deep learning; disease detection; heart diseases; machine learning; ensemble learning; XGBoost

Share and Cite

MDPI and ACS Style

Ogunpola, A.; Saeed, F.; Basurra, S.; Albarrak, A.M.; Qasem, S.N. Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics 2024, 14, 144. https://doi.org/10.3390/diagnostics14020144

AMA Style

Ogunpola A, Saeed F, Basurra S, Albarrak AM, Qasem SN. Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics. 2024; 14(2):144. https://doi.org/10.3390/diagnostics14020144

Chicago/Turabian Style

Ogunpola, Adedayo, Faisal Saeed, Shadi Basurra, Abdullah M. Albarrak, and Sultan Noman Qasem. 2024. "Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases" Diagnostics 14, no. 2: 144. https://doi.org/10.3390/diagnostics14020144

APA Style

Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A. M., & Qasem, S. N. (2024). Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics, 14(2), 144. https://doi.org/10.3390/diagnostics14020144

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