ORIGINAL RESEARCH article
Front. Med.
Sec. Ophthalmology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1591832
Deep Learning for Enhanced Prediction of Diabetic Retinopathy: A Comparative Study on the Diabetes Complications Data Set
Provisionally accepted
- 1Kunming University, Kunming, China
- 2Baoshan People's Hospital, Baoshan, China
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Background: Diabetic retinopathy (DR) screening faces critical challenges in early detection due to its asymptomatic onset and the limitations of conventional prediction models. While existing studies predominantly focus on image-based AI diagnosis, there is a pressing need for accurate risk prediction using structured clinical data. The purpose of this study was to develop, compare, and validate models for predicting retinopathy in diabetic patients via five traditional statistical models and deep learning models.Methods: On the basis of 3,000 data points from the Diabetes Complications Data Set of the National Center for Population Health Sciences Data, the differences in the characteristics of patients with diabetes mellitus and diabetes combined with retinopathy were statistically analyzed using SPSS software. Five traditional machine learning models and a model based on deep neural networks (DNNs) were used to train models to assess retinopathy in diabetic patients.Results: Deep learning-based prediction models outperformed traditional machine learning models, namely logistic regression, decision tree, naive Bayes, random forest, and support vector machine, on all the datasets and performed better in predicting retinopathy in diabetic patients (accuracy, 0.778 vs. 0.753, 0.630, 0.718, 0.758, 0.776, respectively; F1 score, 0.776 vs. 0.751, 0.602, 0.724, 0.755, 0.776, respectively; AUC, 0.833 vs. 0.822, 0.631, 0.769, 0.829, 0.831, respectively). To enhance the interpretability of the deep learning model, SHAP analysis was employed to assess feature importance and provide insights into the key drivers of retinopathy prediction.Deep learning models can accurately predict retinopathy in diabetic patients. The findings of this study can be used for prevention and monitoring by allocating resources to high-risk patients.
Keywords: Diabetic Retinopathy, Deep learning model, Prediction models, Model Comparison, machine learning
Received: 11 Mar 2025; Accepted: 26 May 2025.
Copyright: © 2025 Gong, Pu, Ning, Zhu, Mu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yan Zhu, Kunming University, Kunming, China
Gui Mu, Kunming University, Kunming, China
Jing Li, Kunming University, Kunming, China
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