Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study

In three linesComparative study of 5 classifiers (logistic regression, random forest, XGBoost, SVM, naive Bayes) for chronic kidney disease risk prediction. All achieve AUROC 1.00 internally (UCI, 400 patients) but collapse on external MIMIC-IV data (AUROC 0.48-0.58). Calibration and conformal coverage severely degraded. No model meets clinical deployment criteria.

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