Literature‐informed ensemble machine learning for three‐year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the <scp>PSMMC NephraRisk</scp> model
Bibliographic record
Abstract
INTRODUCTION: Diabetic kidney disease (DKD) and diabetic nephropathy (DN) affect around 40% of diabetic patients but lack accurate risk prediction tools that include social determinants and demographic complexity. We developed and validated an ensemble machine learning model for three-year DKD/DN risk prediction with deployment readiness. METHODS: We analysed 18 742 eligible adult type 2 diabetic patients from Prince Sultan Military Medical City (PSMMC) registry between 2019 and 2024 in Riyadh, Saudi Arabia. Using temporal patient-level splitting, we developed a stacked ensemble model (LightGBM + CoxBoost) with several features including multiple literature-informed imputed variables including family history, non-steroidal anti-inflammatory drug (NSAID) use, socioeconomic deprivation, diabetic retinopathy severity, and antihypertensive medications, imputed via Bayesian multiple imputation by chained equations (MICE) with external study priors. Primary outcome was incident/progressive DKD/DN within 3 years' timeframe. We assessed discrimination, calibration, model utilisation, and algorithmic fairness. RESULTS: The final model achieved excellent discrimination (receiver operating characteristic [AUROC] of 0.852, 95% CI 0.847-0.857) and near-perfect calibration (slope 0.98, intercept -0.012) on multi-trial validation. Decision curve evaluation demonstrated superior net benefit (+22 events prevented per 1000 patients at 10% threshold) compared to treat-all strategies. Bootstrap validation showed minimal optimism in discrimination (C-statistic optimism = 0.005). No algorithmic bias was detected across demographic subgroups (maximum |Δ-AUROC| = 0.010). Prior sensitivity analysis confirmed validity and significance (AUROC variation ≤0.008). The model was engineered and deployed as an interactive web-based application (https://nephrarisk.streamlit.app/). CONCLUSIONS: Our developed and demonstrated model provided accurate and well-fair DKD/DN risk prediction with excellent calibration, allowing for better decision making with deployment as a web-based research tool and framework for future prospective clinical validation. Further validation and testing are warranted from different centres and healthcare systems to increase confidence and dissemination of our model findings for better utilisation purposes in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".