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
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.
Authors
- Tae Keun Yoo (ORCID: https://orcid.org/0000-0003-0890-8614)
- Kyungmin Koh (ORCID: https://orcid.org/0000-0003-0450-6423)
- Daeun Chloe Shin (ORCID: https://orcid.org/0000-0001-9039-9489)
- Ik Hee Ryu (ORCID: https://orcid.org/0009-0007-5213-9249)
- Na Hyeon Yu
Institutions
- Konyang University (KR)
- Kim's Eye Hospital (KR)
- Nune Eye Hospital (KR)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1186/s12911-025-02950-8
- Citations
- 3
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 4.08