Work place: Faculty of Informatics and Computing, University Sultan Zainal Abdlidin Besut Campus Besut, 22200, Terengganu, Malaysia
E-mail: suryani@unisza.ed.my
Website: https://orcid.org/0000-0001-7662-431X
Research Interests:
Biography
Wan Suryani Wan Awang is a senior lecturer in Computer Science at Universiti Sultan Zainal Abidin in Kuala Terengganu, Malaysia, where she has worked since 2007. She earned her undergraduate degree in computer studies from Sheffield Hallam University in the UK, a master’s degree from what are now Universiti Malaysia Terengganu, and a Ph.D. in Computer Science from Cardiff University. Her expertise in distributed systems and databases allows her to offer valuable insights in both academic and professional settings.
By Ahmed Qtaishat Wan Suryani Wan Awangb
DOI: https://doi.org/10.5815/ijieeb.2025.04.04, Pub. Date: 8 Aug. 2025
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.
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