ArticleBMJ open2021
Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Association of estimated glomerular filtration rate with proliferative diabetic retinopathy: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Predicting acute and chronic diabetes complications among adults newly diagnosed with diabetes: development and validation of dynamic risk prediction models.Nature communications · 2026Article
- Predicting Vision-Threatening Retinopathy in Patients With Diabetes Using Patient-Reported Data: Impact on Reading Center Workflow Efficiency.Translational vision science & technology · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Identifying Patients with Rapid Progression to Diabetic Retinopathy in Type 2 Diabetes: A Simple Prediction Model for Community Settings.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Comparison of Interpretable Machine Learning Models Using Systemic Inflammation Index to Predict Preterm Birth in Gestational Diabetes Mellitus.International journal of women's health · 2026Article
- An interpretable machine learning model for detecting vision-threatening diabetic retinopathy among patients with diabetic retinopathy: a web-based cross-sectional study.Frontiers in endocrinology · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Article
- Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.BMC public health · 2025Article
- Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.European journal of medical research · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- Development of machine learning predictive model for type 2 diabetic retinopathy using the triglyceride-glucose index explained by SHAP method.Frontiers in endocrinology · 2025Article
- Deep learning for enhanced prediction of diabetic retinopathy: a comparative study on the diabetes complications data set.Frontiers in medicine · 2025Article
- Development of prediction models for liver metastasis in colorectal cancer based on machine learning: a population-level study.Translational cancer research · 2024Article
- Investigating the relationship between blood metabolites and diabetic retinopathy using two-sample mendelian randomization and in vivo validation.Scientific reports · 2024Article
- Cardiovascular Risk Factors as Independent Predictors of Diabetic Retinopathy in Type II Diabetes Mellitus: The Development of a Predictive Model.Medicina (Kaunas, Lithuania) · 2024Article
- Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.Eye (London, England) · 2024Observational
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset.
designRetrospective study based on a large sample and a high dimensional database.
settingA Chinese central tertiary hospital in Beijing.
participantsInformation on 32 452 inpatients with type-2 diabetes mellitus (T2DM) were retrieved from the electronic medical record system from 1 January 2013 to 31 December 2017.
methodsSixty variables (including demography information, physical and laboratory measurements, system diseases and insulin treatments) were retained for baseline analysis. The optimal 17 variables were selected by recursive feature elimination. The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In order to explain the results of XGBoost model more visually, the Shapley Additive exPlanation (SHAP) method was used.
resultsDR occurred in 2038 (6.28%) T2DM patients. The XGBoost model was identified as the best prediction model with the highest AUC (area under the curve value, 0.90) and showed that an HbA1c value greater than 8%, nephropathy, a serum creatinine value greater than 100 µmol/L, insulin treatment and diabetic lower extremity arterial disease were associated with an increased risk of DR. A patient's age over 65 was associated with a decreased risk of DR.
conclusionsWith better comprehensive performance, XGBoost model had high reliability to assess risk indicators of DR. The most critical risk factors of DR and the cut-off of risk factors can be found by SHAP method to render the output of the XGBoost model clinically interpretable.
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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.