Open Access
ARTICLE
A Modified Gorilla Troops Optimizer-Based Explainable Machine Learning for Early Cardiovascular Disease Prediction
Israt Jahan1, Afsana Begum1, Bibhas Roy Chowdhury Piyas1,*, Fahmid Al Farid2,3,*, Fatama Jannat Tisha1, Shahrin Islam1, Abu Saleh Musa Miah4, Hezerul Abdul Karim3,*
1 Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
2 Faculty of Computer Science and Informatics, Berlin School of Business and Innovation, Berlin, Germany
3 Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Malaysia
4 Department of Computer Science and Engineering, Rajshahi University (RU), Rajshahi, Bangladesh
* Corresponding Author: Bibhas Roy Chowdhury Piyas. Email: 
; Fahmid Al Farid. Email: 
; Hezerul Abdul Karim. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081631
Received 06 March 2026; Accepted 23 April 2026; Published online 08 June 2026
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 Over-sampling 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 fine-tuned 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.
Keywords
Cardiovascular disease (CVD); machine learning; preventive cardiology; class imbalance handling; model optimization; gorilla troops optimizer; UCI heart disease dataset; explainable AI (XAI); SHAP; LIME; ensemble technique; automated diagnosis; clinical decision support system (CDSS); risk stratification
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