Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture.

Diabetes affects 537 million adults globally, a figure projected to reach 783 million by 2045. Despite over 4,200 ML prediction studies, clinical translation remains hindered by an over-reliance on benchmark datasets, unmeasured information costs of multi-source fusion, and untested XAI convergence assumptions. We address these issues by evaluating 353,680 records (from 455,446 candidates) across a Clinical-Biomarker Set (CBS) and a Lifestyle-Survey Set (LSS) using a strict leakage-free protocol. Under this protocol, normalisation, imputation, and SMOTE synthesis were fitted exclusively on training data. We formally define the Imputation Paradox as the AUC loss that occurs when critical diagnostic biomarkers are median-imputed under structural MNAR missingness. An Optuna-tuned stacking ensemble (Random Forest, XGBoost, LightGBM) achieves an AUC-ROC of 0.8481 on a held-out 70,736-sample test set. While a simple average baseline marginally exceeded stacking on pure discrimination (AUC 0.8521), stacking was retained for its superior precision (0.3439) and interpretable meta-learner trust weights, yielding a cross-validation AUC of 0.8491 ± 0.0014 and a Wilcoxon-significant improvement over baselines (