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

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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Publication Details

Journal
Frontiers in Medicine
Published
2025-05-30
DOI
https://doi.org/10.3389/fmed.2025.1542860
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Soo Kyung Lim, Se Joon Woo, Young Wook Choi, Min Seok Kim et al.
Frontiers in Medicine
Retinal Imaging and Analysis
article

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

Soo Kyung Lim, Se Joon Woo, Young Wook Choi, Min Seok Kim, Youngju Lee, B Prakash
article en

Abstract

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.

Frontiers in MedicineVol. 12
Seoul National University Bundang Hospital (KR), Korea Pharma (South Korea) (KR)
Ministry of Trade, Industry and Energy
Openalex Percentile: Top 81%
Retinal Imaging and Analysis
DOIOpen Access PDFOpenAlex
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A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021) — Soo Kyung Lim, Se Joon Woo, et al. · Frontiers in Medicine (2025) | TGRS Research Map | TGRS