ArticleJournal of diabetes research2024
Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.
Article in Journal of diabetes research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.Chinese medical journal · 2026Article
- Association of Estimated Pulse Wave Velocity with Chronic Kidney Disease Risk: A Machine Learning Analysis Based on NHANES and CHARLS.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Interpretable machine learning for predicting major amputation risk in hospitalized diabetic foot ulcer patients: a single-center study with temporal external validation.Frontiers in endocrinology · 2026Article
- Development of an explainable machine learning model for predicting the occurrence of advanced diabetic kidney disease.Frontiers in endocrinology · 2026Article
- From explainability to clinical actionability: translating artificial intelligence models into decision support for endocrine disease management.Frontiers in endocrinology · 2026Review
- A high-risk sepsis subtype identified by regulated cell death signatures and MAD2L2 validation.Frontiers in immunology · 2026Article
- Explainable Artificial Intelligence for Early Diabetic Kidney Disease Risk Profiling in Type 2 Diabetes: Translational Readiness and Clinical Boundaries.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Machine learning screening of risk factors for diabetic microvascular complications and construction of a gradient boosting decision tree predictive model.Frontiers in endocrinology · 2026Article
- Development and validation of an explainable machine learning model for predicting interstitial fibrosis and tubular atrophy in biopsy-confirmed diabetic nephropathy.Frontiers in endocrinology · 2026Article
- Are we ready to integrate advanced artificial intelligence models in clinical laboratory?Biochemia medica · 2025Review
- Development and validation of predictive models for diabetic retinopathy using machine learning.PloS one · 2025Article
- Multi-feature integrated machine learning prediction model for early nephropathy in elderly living with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.Frontiers in endocrinology · 2025Article
- Artificial intelligence applied to diabetes complications: a bibliometric analysis.Frontiers in artificial intelligence · 2025Article
- Predictive efficacy assessment of serum βAmerican journal of translational research · 2025Article
- Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach.Preventive medicine reports · 2024Article
- Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability.Endocrine · 2024Article
- Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.Journal of diabetes research · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.