HFDE–CKD: A Hyper-Fidelity Dynamic Ensemble Framework for Chronic Kidney Disease Diagnosis

Chronic kidney disease (CKD) is a global health disease that must be recognized and monitored early to prevent major consequences and improve the health of patients, yet modern gradient boosting algorithms often produce dangerous false positives due to overconfidence and static ensemble weighting. To address this, we propose the Hyper-Fidelity Dynamic Ensemble framework for CKD diagnosis (HFDE–CKD). Our approach utilizes a rigid quad-fold data isolation protocol and applies Standard SMOTE to the training manifold. By removing direct diagnostic identifiers (e.g., eGFR, creatinine, and albumin), the framework is forced to rely on underlying early indications. We quantify predictive doubt using Shannon entropy and inter-model variance, concatenating these measurements with calibrated probability vectors into a multidimensional uncertainty matrix optimized via a logistic meta-learner. Evaluated on an imbalanced dataset of 11,933 records, HFDE–CKD achieved an accuracy of 0.8596, a sensitivity of 0.8900, and an F1-score of 0.8979. Furthermore, HFDE–CKD’s superiority over the best-performing baseline was confirmed through McNemar’s test, which was statistically significant (p<0.001). Emphasizing diagnostic safety, the model yielded a Brier score of 0.1012, an NLL of 0.3225, and a specificity of 0.7907, significantly reducing the risk of false positives. Furthermore, ablation testing confirmed the advantage of uncertainty injection, while SHAP analysis validated clinical dependency on early metabolic biomarkers. The proposed HFDE–CKD framework systematically leverages epistemic uncertainty to bypass the traditional sensitivity-versus-false-positive trade-off, providing a precisely calibrated, robust decision-support tool for early CKD screening.