Citation

Jahan, Israt and Begum, Afsana and Piyas, Bibhas Roy Chowdhury and Farid, Fahmid Al and Tisha, Fatama Jannat and Islam, Shahrin and Miah, Abu Saleh Musa and Karim, Hezerul Abdul (2026) A Modified Gorilla Troops Optimizer-Based Explainable Machine Learning for Early Cardiovascular Disease Prediction. Computers, Materials & Continua, 88 (3). pp. 1-10. ISSN 1546-2226

Text A Modified Gorilla Troops Optimizer-Based Explainable Machine Learning for Early Cardiovascular Disease Prediction.pdf - Published Version Restricted to Repository staff only Download (11MB)

Abstract

Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare. Despite advances in cardiology, early-stage cardiovascular disease often remains undetected, which hinders timely intervention and leads to preventable deaths. To overcome this problem, this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease (CVD). Initially, this study examined several data-balancing strategies, for example, SMOTE (Synthetic Minority Oversampling Technique), SMOTETomek (Synthetic Minority Over-sampling Technique + Tomek Links), Tomek Links, ADASYN (Adaptive Synthetic Sampling), and SMOTE-ENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) within the data-preprocessing pipeline. We proposed a novel Adaptive Inertia Weight Gorilla Troops Optimizer (AIW-GTO) to overcome classical GTO’s (Gorilla Troops Optimizer) unstable convergence by adaptively controlling step sizes. It uses large exploratory steps early for wide search and smaller steps later for finetuned local optimization, which ensures stable convergence and enhanced optimization accuracy. Several machine learning techniques, namely XGBoost, Random Forest, SVM (Support Vector Machine), LightGBM (Light Gradient Boosting Machine), and MLP (Multilayer Perceptron) classifier, were evaluated on the multi-regional UCI heart disease dataset. The experimental findings revealed that, by integrating AIW-GTO Optimization and class imbalance mitigation, LightGBM and XGBoost individually achieved a benchmark accuracy of 93.48% and 91.85%, respectively. Moreover, a weighted ensemble of them further improved the accuracy to 94.02%. Sensitivity analysis further evaluated the model’s ability to perform under incomplete clinical test data. To enhance ethical considerations and clinical trust, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were utilized to provide model explainability and identify the most influential features affecting prediction outcomes. Analysis indicated that ECG-related (Electrocardiogram) features, including ST_Slope (exercise-induced ST change) and Oldpeak (ST depression magnitude), emerged as key predictors of CVD risk. Overall, the proposed framework provides a clinically reliable and interpretable approach for early cardiovascular risk assessment to enable proactive patient management.

Item Type:	Article
Uncontrolled Keywords:	Cardiovascular disease (CVD), machine learning, preventive cardiology
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 06:02
Last Modified:	02 Sep 2026 06:02
URII:	http://shdl.mmu.edu.my/id/eprint/16525

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