Key result
WaOA-MRFO algorithm matches baseline AUC for heart disease prediction while cutting the feature set ~61%.
Why the study?
Existing wrapper-based metaheuristic selectors for cardiovascular disease prediction operate as opaque black boxes, failing to reveal how clinical features are selected or whether subsets remain consistent across data samples.
Does the WaOA-MRFO hybrid optimizer improve feature selection and maintain predictive accuracy for heart disease prediction compared to baseline methods?
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.
Enables interpretable CVD prediction models with fewer features; leaves open prospective validation before clinical use.
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Adamu et al. (2026) studied 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%.
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