A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021)
BackgroundMachine learning technology that uses available clinical data to predict diabetic retinopathy (DR) can be highly valuable in medical settings where fundus cameras are not accessible.ObjectiveThis study aimed to develop and compare machine learning algorithms for predicting DR without fundu...
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Frontiers Media S.A.
2025-05-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1542860/full |
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| author | Min Seok Kim Young Wook Choi Borghare Shubham Prakash Youngju Lee Soo Lim Se Joon Woo |
| author_facet | Min Seok Kim Young Wook Choi Borghare Shubham Prakash Youngju Lee Soo Lim Se Joon Woo |
| author_sort | Min Seok Kim |
| collection | DOAJ |
| description | BackgroundMachine learning technology that uses available clinical data to predict diabetic retinopathy (DR) can be highly valuable in medical settings where fundus cameras are not accessible.ObjectiveThis study aimed to develop and compare machine learning algorithms for predicting DR without fundus image.MethodsWe used data from Korea National Health and Nutrition Examination Survey (2008–2012 and 2017–2021) and enrolled individuals aged ≥ 20 years with diabetes who received fundus examination. Predictive models for DR were developed using logistic regression and three machine learning algorithms: extreme gradient boosting, decision tree, and random forest. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and accuracy for the diagnosis of DR, and feature importance was determined using Shapley Additive Explanations (SHAP).ResultsAmong the 3,026 diabetic participants (male, 50.7%; mean age, 63.7 ± 10.5 years), 671 (22.2%) had DR. The random forest model, using 16 variables, achieved the highest AUC of 0.748 (95% confidence interval, 0.705–0.790) with a sensitivity 0.669, specificity of 0.729 and an accuracy of 0.715. As interpreted by SHAP, HbA1c, fasting glucose levels, duration of diabetes, and body mass index were identified as common key determinants influencing the model’s outcomes.ConclusionThe DR prediction models using machine learning techniques demonstrated reliable performance even without fundus imaging, with the random forest model showing particularly strong results. These models could assist in managing DR by identifying high-risk patients, enabling timely ophthalmic referrals. |
| format | Article |
| id | doaj-art-5e095302735e49389ae730caef44cb61 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-5e095302735e49389ae730caef44cb612025-08-20T03:21:47ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15428601542860A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021)Min Seok Kim0Young Wook Choi1Borghare Shubham Prakash2Youngju Lee3Soo Lim4Se Joon Woo5Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of KoreaRetiMark R&D Center, Seoul, Republic of KoreaRetiMark R&D Center, Seoul, Republic of KoreaRetiMark R&D Center, Seoul, Republic of KoreaDepartment of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Republic of KoreaDepartment of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of KoreaBackgroundMachine learning technology that uses available clinical data to predict diabetic retinopathy (DR) can be highly valuable in medical settings where fundus cameras are not accessible.ObjectiveThis study aimed to develop and compare machine learning algorithms for predicting DR without fundus image.MethodsWe used data from Korea National Health and Nutrition Examination Survey (2008–2012 and 2017–2021) and enrolled individuals aged ≥ 20 years with diabetes who received fundus examination. Predictive models for DR were developed using logistic regression and three machine learning algorithms: extreme gradient boosting, decision tree, and random forest. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and accuracy for the diagnosis of DR, and feature importance was determined using Shapley Additive Explanations (SHAP).ResultsAmong the 3,026 diabetic participants (male, 50.7%; mean age, 63.7 ± 10.5 years), 671 (22.2%) had DR. The random forest model, using 16 variables, achieved the highest AUC of 0.748 (95% confidence interval, 0.705–0.790) with a sensitivity 0.669, specificity of 0.729 and an accuracy of 0.715. As interpreted by SHAP, HbA1c, fasting glucose levels, duration of diabetes, and body mass index were identified as common key determinants influencing the model’s outcomes.ConclusionThe DR prediction models using machine learning techniques demonstrated reliable performance even without fundus imaging, with the random forest model showing particularly strong results. These models could assist in managing DR by identifying high-risk patients, enabling timely ophthalmic referrals.https://www.frontiersin.org/articles/10.3389/fmed.2025.1542860/fulldiabetic retinopathymachine learningrandom forest algorithmsKoreaprediction |
| spellingShingle | Min Seok Kim Young Wook Choi Borghare Shubham Prakash Youngju Lee Soo Lim Se Joon Woo A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) Frontiers in Medicine diabetic retinopathy machine learning random forest algorithms Korea prediction |
| title | A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) |
| title_full | A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) |
| title_fullStr | A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) |
| title_full_unstemmed | A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) |
| title_short | A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) |
| title_sort | machine learning based prediction of diabetic retinopathy using the korea national health and nutrition examination survey 2008 2012 2017 2021 |
| topic | diabetic retinopathy machine learning random forest algorithms Korea prediction |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1542860/full |
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