International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 520KB)
Enhanced Predictive Modelling of Heart Disease Using Optimized Machine Learning Algorithms
PDF (520KB), PP.41-52
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Author(s)
Ahmed Qtaishat
1,2,*
Wan Suryani Wan Awangb
3
Index Terms
Machine Learning, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest, Extra Tree Classifier, Gradient Boosting
Abstract
Cardiovascular disease (CVD) remains a leading global cause of mortality, underscoring the importance of its early detection. This research leverages advanced Machine Learning (ML) algorithms to predict Coronary Heart Disease (CHD) risk by analysing critical factors. A comprehensive evaluation of ten ML techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), AdaBoost, Multi-Layer Perceptron Neural Network (MLPNN), and Extremely Randomized Trees (ERT), was conducted. The ERT algorithm demonstrated superior performance, achieving the highest test accuracy of 88.52%, with precision, recall, and F1-scores of 0.89, 0.88, and 0.88, respectively, for class 0 (no CHD), and 0.88, 0.91, and 0.89, respectively, for class 1 (CHD). The model was optimized using hyperparameters such as a bootstrap setting of False, no maximum depth, a minimum sample split of 2, a minimum leaf size of 4, and 300 estimators. This study provides a detailed comparison of these techniques using metrics such as precision, recall, and F1-score, offering critical insights for optimizing predictive models in clinical applications. By advancing early detection methodologies, this work aims to support healthcare practitioners in reducing the global burden of cardiac diseases.
Cite This Paper
Ahmed Qtaishat, Wan Suryani Wan Awangb, "Enhanced Predictive Modelling of Heart Disease Using Optimized Machine Learning Algorithms", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.4, pp. 41-52, 2025. DOI:10.5815/ijieeb.2025.04.04
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