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

Background: 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.

Objective: This study aimed to develop and compare machine learning algorithms for predicting DR without fundus image.

Methods: 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.