Missing-data strategies are often compared using a single cohort or prediction model, making it difficult to distinguish imputation-specific effects from benefits due to the downstream learner or prediction averaging. We conducted a leakage-controlled factorial simulation in two public cardiac cohorts (n = 299 and n = 918), comparing six strategies across three missingness mechanisms, three missingness rates (10–50%), ten seeds, and three learners. A four-arm analysis further examined the contribution of multiple imputation relative to single imputation and model averaging. Strategy effects depended on the learner: exploratory strategy-by-learner interactions were significant in both cohorts, while no strategy effect survived Holm correction for penalised logistic regression. Prediction-layer-pooled multiple imputation ranked first for the tree-based learners in the larger cohort. In the smaller cohort with XGBoost, its paired median AUROC difference versus a bagged mean-imputation ensemble was −0.003 (Holm-adjusted p = 0.275); because equivalence was not tested, this result does not establish that the strategies perform equivalently. No consistent strategy-by-missingness-mechanism interaction was found. These findings support evaluating missing-data handling jointly with the prediction model and interpreting apparent strategy advantages in light of ensemble effects. This research offers new insights into strategies for handling missing data.