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

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 machi...

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Published in:	BMC medical informatics and decision making Vol. 25; no. 1; pp. 118 - 14
Main Authors:	Yu, Na Hyeon, Shin, Daeun, Ryu, Ik Hee, Yoo, Tae Keun, Koh, Kyungmin
Format:	Journal Article
Language:	English
Published:	London BioMed Central 07.03.2025 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Adult Aged Alcohol use Algorithms Artificial neural networks Biomarkers Blood pressure Blood tests Body mass index Cardiovascular disease Code-free tool Coding Confidence intervals Cross-Sectional Studies Data collection Data mining Data Mining - methods Datasets Demographic aspects Diabetes Family income Feasibility studies Female Fundus Oculi Health Informatics Health risks Humans Hypertension Information Systems and Communication Service Laboratories Laboratory tests Learning algorithms Machine Learning Male Management of Computing and Information Systems Medical history Medicine Medicine & Public Health Metabolic disorders Middle Aged Neural coding Neural networks Neural Networks, Computer No-code machine learning Nutrition Nutrition Surveys Occlusion Open source medical software Osteoarthritis Osteoporosis Photography Prediction models Regression analysis Republic of Korea - epidemiology Retina Retinal diseases Retinal images Retinal vein occlusion Retinal Vein Occlusion - diagnosis Retinal Vein Occlusion - epidemiology Review boards Risk assessment Risk Assessment - methods Risk factors Source code Statistical analysis Tables (data) Technology application Veins Veins & arteries Workflow Republic of Korea South Korea Germany Retinal vein occlusion Code-free tool No-code machine learning Risk factors
ISSN:	1472-6947, 1472-6947
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Summary:	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, component-based tool with a user-friendly interface, and Google Vertex AI. An easy-to-use oversampling function was employed to address class imbalance, enhancing the usability of the workflow. Various machine learning algorithms were trained by incorporating all features from the health check-up data in the development set. The primary outcome was the area under the receiver operating characteristic curve (AUC) for identifying RVO. Results All machine learning training was completed without the need for coding experience. An artificial neural network (ANN) with a ReLU activation function, developed using Orange Data Mining, demonstrated superior performance, achieving an AUC of 0.856 (95% confidence interval [CI], 0.835–0.875) in internal validation and 0.784 (95% CI, 0.763–0.803) in external validation. The ANN outperformed logistic regression and Google Vertex AI models, though differences were not statistically significant in internal validation. In external validation, the ANN showed a marginally significant improvement over logistic regression ( P = 0.044), with no significant difference compared to Google Vertex AI. Key predictive variables included age, household income, and blood pressure-related factors. Conclusion This study demonstrates the feasibility of developing an accessible, cost-effective RVO risk prediction tool using health check-up data and no-code machine learning platforms. Such a tool has the potential to enhance early detection and preventive strategies in general healthcare settings, thereby improving patient outcomes.
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ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-025-02950-8