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

2025-12-01 · Source: cs.LG updates on arXiv.org · Field: Health & Wellbeing — Clinical Care & Medical Practice, Medical Devices & Health Technology, Health & Medical Research · Depth: Expert, extended

Summary

A study evaluated five machine learning classifiers—logistic regression, random forest, XGBoost, support vector machine with Platt scaling, and Gaussian naive Bayes—for chronic kidney disease (CKD) risk prediction. Models were trained on the UCI CKD dataset (400 patients, 62.5% CKD) and stress-tested on the MIMIC-IV demo cohort (97 patients, 23.7% CKD) to assess performance under distributional shift. While all models achieved AUROC 1.00 on the internal UCI test set, and post-isotonic Expected Calibration Error (ECE) dropped to 0.000–0.022, performance severely deteriorated on the external cohort. On MIMIC-IV, AUROC fell to 0.48–0.58, ECE surged to 0.68–0.76, and conformal prediction coverage collapsed from 0.80–0.98 to 0.21–0.25, far below the 90% target. Furthermore, no model passed an eight-criterion deployment readiness framework, with scores ranging from 2 to 4 out of 16, highlighting that strong internal metrics do not guarantee reliability under real-world data shifts.

Key takeaway

For MLOps Engineers or Research Scientists deploying clinical ML models, you must prioritize robust external validation beyond internal metrics. Your models, even with perfect internal AUROC and calibration, will likely fail under real-world prevalence shifts and missing features. Implement a structured deployment readiness framework that includes calibration stability, conformal prediction coverage, and subgroup equity to ensure your models are trustworthy and safe for clinical use.

Key insights

Perfect internal ML metrics do not guarantee reliability under real-world data distribution shifts.

Principles

• Calibration stability is critical for clinical ML trustworthiness.
• Conformal prediction signals distribution shift directly.
• External validation must include prevalence and feature shifts.

Method

A framework evaluated five classifiers across calibration quality, conformal prediction coverage, and an eight-criterion deployment readiness checklist on internal and external data.

In practice

• Assess calibration stability and conformal coverage transfer.
• Implement a multi-criterion deployment readiness checklist.
• Stress-test models with external cohorts for distributional shifts.

Topics

• Chronic Kidney Disease
• Machine Learning Models
• Probability Calibration
• Conformal Prediction
• Deployment Readiness
• External Validation
• Distributional Shift

Best for: AI Product Manager, AI Scientist, Research Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.