Authors - Tirupathi Rao Dockara, Manisha Malhotra Abstract - The prediction of cardiovascular disease (CVD) risk by machine learning is frequently impeded by duplicated and associated clinical characteristics, leading to complex and less robust models. Feature selection is therefore essential to improve model compactness while maintaining predictive performance. This study presents a systematic evaluation of meta-heuristic-based feature selection for CVD risk modeling under a standardized experimental setting. Feature selection is formulated as a wrapper-based optimization problem and evaluated using representative population-based meta-heuristic algorithms from multiple families. All methods are assessed using the XGBoost Histogram classifier on a public cardiovascular dataset comprising approximately 70,000 records with 13 clinical features. Experimental results show that meta-heuristic feature selection consistently reduces the number of input features by more than 60% while achieving comparable predictive performance across different algorithmic families. In addition, SHAP analysis is employed to examine the contributions of the selected features and support model interpretability.
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