ArticleRenal failure2025
Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.Diabetes, obesity & metabolism · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Predicting 1-Year Renal Outcomes in Patients with Diabetic Kidney Disease in CKD Stages 3 to 4: A Multimodal Machine Learning Approach Fusing Clinical Composites and Pathology Images.Research (Washington, D.C.) · 2026Article
- Identification and validation of an explainable prediction model of favorable outcome under integrative medicine treatment exposure in DKD adult patients: a retrospective cohort study.Frontiers in digital health · 2026Article
- Integrated multi-omics analysis unveils microbiota-metabolite-host interactions and novel biomarkers for early diabetic kidney disease diagnosis.Frontiers in immunology · 2026Article
- Construction and validation of a hypoglycemia risk prediction model for hospitalized type 2 diabetes patients based on machine learning.BMC endocrine disorders · 2025Article
- Analysis of factors influencing innovative behaviors of intensive care unit nurses using a random forest model-a multicentre cross-sectional study.BMC nursing · 2025Article
- Predictive efficacy assessment of serum βAmerican journal of translational research · 2025Article
- A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.Frontiers in surgery · 2025Article
- AI Foundations in China's Medical Physiology Education: Pedagogical Practices and Systemic Challenges.Advances in medical education and practice · 2025Article
- Zinc deficiency predicts new-onset diabetic kidney disease in type 2 diabetes: a retrospective cohort study.Frontiers in nutrition · 2025Article
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5 authors.
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Abstract
backgroundDiabetic kidney disease (DKD) is a common and serious complication of diabetic mellitus (DM). More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine learning algorithms (MLAs) in patients with type 2 DM (T2DM).
methodsThe electronic health records of 12,190 T2DM patients with 3-year follow-ups were extracted, and the dataset was divided into a training and testing dataset in a 4:1 ratio. The risk variables for DKD development were ranked and selected to establish forecasting models. The performance of models was further evaluated by the indexes of sensitivity, specificity, positive predictive value, negative predictive value, accuracy, as well as F1 score, using the testing dataset. The value of accuracy was used to select the optimal model.
resultsUsing the importance ranking in the random forest package, the variables of age, urinary albumin-to-creatinine ratio, serum cystatin C, estimated glomerular filtration rate, and neutrophil percentage were selected as the predictors for DKD onset. Among the seven forecasting models constructed by MLAs, the accuracy of the Light Gradient Boosting Machine (LightGBM) model was the highest, indicated that the LightGBM algorithms might perform the best for predicting 3-year risk of DKD onset.
conclusionsOur study could provide powerful tools for early DKD risk prediction, which might help optimize intervention strategies and improve the renal prognosis in T2DM patients.
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