ArticleBMC medical informatics and decision making2013
Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study.
Article in BMC medical informatics and decision making, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Systematic reviews · 2021Pooled it
- Detection of referable diabetic retinopathy using machine learning on routine clinical data.Frontiers in medicine · 2026Article
- Machine learning-based prediction of diabetic retinopathy from pupillary abnormalities in a South Indian population.PloS one · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Prevalence and risk factors of hypertensive retinopathy in South Korea based on national health survey data.Scientific reports · 2025Article
- Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.BMC medical informatics and decision making · 2025Article
- Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.Journal of Crohn's & colitis · 2025Article
- Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging.International journal of retina and vitreous · 2025Article
- Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4.Medical & biological engineering & computing · 2025Article
- Deep learning for enhanced prediction of diabetic retinopathy: a comparative study on the diabetes complications data set.Frontiers in medicine · 2025Article
- A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008-2012, 2017-2021).Frontiers in medicine · 2025Article
- Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.Eye (London, England) · 2024Observational
- Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels.Intelligence-based medicine · 2024Article
- Development and Validation of a Diabetic Retinopathy Risk Stratification Algorithm.Diabetes care · 2023Article
- Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review.BMC ophthalmology · 2023Review
- Predicting Postoperative Anterior Chamber Angle for Phakic Intraocular Lens Implantation Using Preoperative Anterior Segment Metrics.Translational vision science & technology · 2023Article
- Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies.World journal of diabetes · 2022Review
- Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?Life (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBlindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients.
methodsHealth records from the Korea National Health and Nutrition Examination Surveys (KNHANES) V-1 were used. The prediction models for DR were constructed using data from 327 diabetic patients, and were validated internally on 163 patients in the KNHANES V-1. External validation was performed using 562 diabetic patients in the KNHANES V-2. The learning models, including ridge, elastic net, and LASSO, were compared to the traditional indicators of DR.
resultsConsidering the Bayesian information criterion, LASSO predicted DR most efficiently. In the internal and external validation, LASSO was significantly superior to the traditional indicators by calculating the area under the curve (AUC) of the receiver operating characteristic. LASSO showed an AUC of 0.81 and an accuracy of 73.6% in the internal validation, and an AUC of 0.82 and an accuracy of 75.2% in the external validation.
conclusionThe sparse learning model using LASSO was effective in analyzing the epidemiological underlying patterns of DR. This is the first study to develop a machine learning model to predict DR risk using health records. LASSO can be an excellent choice when both discriminative power and variable selection are important in the analysis of high-dimensional electronic health records.
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