Identification of Significant Risk Factors and Robust Cardiovascular Disease Prediction Using a CLPO-Optimization-Based Framework

Cardiovascular disease (CVD) remains the leading cause of global mortality, underscoring the urgent need for predictive frameworks that are both accurate and clinically interpretable. This study introduces a hybrid diagnostic framework that integrates the novel Competitive Learning and Past-Based Optimization (CLPO) algorithm with the XGBoost classifier to enhance CVD risk prediction and biomarker identification. Unlike conventional optimization-based approaches, CLPO employs past-informed learning, competitive interactions, and an adaptive escape mechanism to dynamically balance exploration and exploitation, ensuring stable convergence and efficient hyperparameter tuning. Experimental evaluations on benchmark cardiovascular datasets achieved an impressive 94.79% cross-validation accuracy, F1-score of 0.9509, and AUC of 0.965, with a recall of 0.955, confirming the model’s ability to minimize false negatives—a critical factor in clinical screening. The framework also demonstrated computational efficiency, with an average runtime of 203.8 ± 57.4 s, ranking first overall among nine comparative optimizers. SHAP-based explainability analysis revealed ST slope, chest pain type, and cholesterol as the most predictive risk factors, offering transparent and clinically meaningful interpretations. CLPO-XGBoost consistently achieved highly competitive diagnostic accuracy and superior algorithmic stability compared with prominent recent state-of-the-art frameworks. These findings position the proposed framework as a next-generation predictive cardiology model that unites high diagnostic accuracy, computational efficiency, and interpretability—bridging the gap between artificial intelligence and clinical decision support.