Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China
Open accessAbstract
Objective: Aiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset. Design: Retrospective study based on a large sample and a high dimensional database. Setting: A Chinese central tertiary hospital in Beijing. Participants: Information on 32 452 inpatients with type-2 diabetes mellitus (T2DM) were retrieved from the electronic medical record system from 1 January 2013 to 31 December 2017. Methods: Sixty variables (including demography information, physical and laboratory measurements, system diseases and insulin treatments) were retained for baseline analysis. The optimal 17 variables were selected by recursive feature elimination. The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In order to explain the results of XGBoost model more visually, the Shapley Additive exPlanation (SHAP) method was used. Results: DR occurred in 2038 (6.28%) T2DM patients. The XGBoost model was identified as the best prediction model with the highest AUC (area under the curve value, 0.90) and showed that an HbA1c value greater than 8%, nephropathy, a serum creatinine value greater than 100 µmol/L, insulin treatment and diabetic lower extremity arterial disease were associated with an increased risk of DR. A patient’s age over 65 was associated with a decreased risk of DR. Conclusions: With better comprehensive performance, XGBoost model had high reliability to assess risk indicators of DR. The most critical risk factors of DR and the cut-off of risk factors can be found by SHAP method to render the output of the XGBoost model clinically interpretable.
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Li et al. built a DR risk prediction model with XGBoost, identifying key features such as age, FBG, and HbA1c.
“Li et al. [15] built a DR risk prediction model with XGBoost, identifying key features such as age, FBG, and HbA1c.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [15]
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- Authors
- Li, W., Song, Y., Chen, K., Ying, J., Zheng, Z., Qiao, S., Yang, M., Zhang, M., & Zhang, Y.
- Affiliations
- Medical School of Chinese PLA, Beijing, China
- Chinese PLA General Hospital (CN)
- Central Military Commission (CN)
- Corresponding author
- Zhang, Ying
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2021-11
- Volume
- 11
- Issue
- 11
- Pages
- e050989
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC8628336
- OpenAlex
- W3215470728
- Cited by
- 76
- References
- 53
- Citations by year
- 2026: 8 · 2025: 23 · 2024: 23 · 2023: 14 · 2022: 8
- FWCI
- 10.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Open (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Chinese PLA general hospital medical big data program (2017MBD-020)
- Topics
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- diabetic retinopathy, type 2 diabetes, risk factors, XGBoost, SHAP, risk prediction model, HbA1c, diabetic nephropathy, serum creatinine, insulin therapy, model interpretability
- MeSH terms
- Machine Learning, China, Diabetes Mellitus, Diabetic Retinopathy, Humans, Retrospective Studies, Reproducibility of Results, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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