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.

ABSTRACT:

Background

Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease risk assessment. This study aimed to develop a machine learning model to predict RVO risk in the general population using such tabular health data, without requiring coding expertise or retinal imaging.

Methods

We utilized data from the Korea National Health and Nutrition Examination Surveys (KNHANES) collected between 2017 and 2020 to develop the RVO prediction model, with external validation performed using independent data from KNHANES 2021. Model construction was conducted using Orange Data Mining, an open-source, code-free, co

SUBMITTER: Yu NH 

PROVIDER: S-EPMC11889835 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature