The WaOA-MRFO algorithm achieved an AUC of 0.843, statistically equivalent to the all-features baseline (AUC 0.847, p=0.619), while reducing the feature set by 61%.
Does the WaOA-MRFO hybrid optimizer improve feature selection and maintain predictive accuracy for heart disease prediction compared to baseline methods?
The WaOA-MRFO hybrid optimizer effectively reduces feature dimensionality by 61% while maintaining predictive accuracy and cross-institutional transferability for heart disease prediction.
Absolute Event Rate: 0.843% vs 0.847%
p-value: p=0.619
Abstract Cardiovascular disease prediction demands accurate models alongside feature selection processes that are transparent, reproducible, and diagnostically interpretable. Existing wrapper-based metaheuristic selectors operate as black boxes, reporting only final accuracy without revealing how features are selected or whether subsets remain consistent across data samples. We propose WaOA-MRFO, an adaptive hybrid optimizer that combines Walrus Optimization Algorithm exploration with Manta Ray Foraging Optimization exploitation through stagnation-aware diversity control. A structured iteration-level diagnostic suite monitors population diversity, convergence behaviour, and feature selection stability, making the search dynamics directly observable rather than opaque. Under 5 × 5 nested cross-validation on the Cleveland heart disease cohort (n=261 n = 261), WaOA-MRFO achieves an AUC of 0. 843 0. 056 0. 843 ± 0. 056 — statistically equivalent to the all-features baseline (AUC =0. 847 = 0. 847, p=0. 619 p = 0. 619) — while reducing the feature set by 61\% 61 %. The method matches LASSO and recursive feature elimination on identical splits and significantly outperforms uninformed selection at matched dimensionality (AUC=+0. 091 Δ AUC = + 0. 091, p p 0. 001). A stable four-feature core (exang, ca, thalch, oldpeak) emerges in more than 70\% 70 % of folds, while the top two features are selected in every fold, indicating reproducible signal extraction. EvoMapX operator attribution analysis identifies the WaOA spiral operator as the primary convergence driver, accounting for 66. 7\% 66. 7 % of improvement events, while the Population Evolution Graph reveals the progressive stabilisation of the informative feature core across iterations. Fixed-mask transfer evaluation on an independent three-site cohort (n=616 n = 616) yields AUC =0. 811 0. 041 = 0. 811 ± 0. 041, confirming cross-institutional transferability of the stable-tier features. Ablation analysis identifies bit-flip mutation as the sole statistically significant contributor to subset consistency (p=0. 028 p = 0. 028, Cohen’s d = 0. 531 <mml: math xmlns: mml="http: //www
Adamu et al. (Mon,) conducted a other in Heart disease (n=877). WaOA-MRFO adaptive hybrid optimizer vs. All-features baseline was evaluated on Area under the ROC curve (AUC) (p=0.619). The WaOA-MRFO algorithm achieved an AUC of 0.843, statistically equivalent to the all-features baseline (AUC 0.847, p=0.619), while reducing the feature set by 61%.