aceMachine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD)
Mortality prediction models for patients with non-dialysis chronic kidney disease (CKD) remain limited despite their clinical importance. While machine learning (ML) offers the potential to improve prediction accuracy, its "black-box" nature has hindered clinical adoption. This study aimed to develop and validate an interpretable ML model for predicting 5-year all-cause mortality in patients with non-dialysis CKD and to deploy it as a user-friendly web-based risk stratification tool. We analyzed 1,858 patients (…
A CatBoost model trained on 1,858 non-dialysis CKD patients predicted 5-year all-cause mortality with AUC 0.813, outperforming logistic regression, and a simplified 5-feature version maintained AUC 0.795 in external validation.
Machine learning's black-box nature has hindered clinical adoption of mortality prediction models for non-dialysis CKD despite their clinical importance.
Evidence
- Peer-reviewedKidney Research and Clinical Practice2026-08-07
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Truvace Impact Record TRV-2026-0688, v1: “Machine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD).” Truvace, 2026-08-08. /record/TRV-2026-0688 (accessed at citation time). sha256 4591b50000d05bef…
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