A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021)

Machine 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. This study aimed to develop and compare machine learning algorithms for predicting DR without fundus image. We used d...

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Veröffentlicht in:Frontiers in medicine Jg. 12; S. 1542860
Hauptverfasser: Kim, Min Seok, Choi, Young Wook, Prakash, Borghare Shubham, Lee, Youngju, Lim, Soo, Woo, Se Joon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 2025
Schlagworte:
diabetic retinopathy
Korea
machine learning
prediction
random forest algorithms
prediction
random forest algorithms
diabetic retinopathy
machine learning
Korea
ISSN:2296-858X, 2296-858X
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Zusammenfassung:Machine 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. This study aimed to develop and compare machine learning algorithms for predicting DR without fundus image. We 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). Among 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. The 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.
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ISSN:2296-858X
2296-858X
DOI:10.3389/fmed.2025.1542860