Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms
The XGBoost-based prediction model effectively assesses DR risk in T2DM patients using routine laboratory data, aiding clinicians in identifying high-risk individuals and guiding personalized management strategies, especially in medically underserved areas.
Authors
- Yan‐Wei Hu
- Biao Song
- Ruihuan Zhang (ORCID: https://orcid.org/0000-0003-4486-2916)
- Xiaohua Wan (ORCID: https://orcid.org/0000-0002-6078-405X)
- Wei Wei (ORCID: https://orcid.org/0000-0002-5905-5482)
- Lin Zhang (ORCID: https://orcid.org/0000-0001-7826-2850)
- Yanan Wang (ORCID: https://orcid.org/0000-0001-7179-4686)
Institutions
- Beijing Tongren Hospital (CN)
- Capital Medical University (CN)
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
- National Center for Clinical Laboratories (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1186/s40001-025-02442-5
- Citations
- 9
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 12.24
Funders
- National Natural Science Foundation of China