Gong Weijun, Pu You, Ning Tiao, Zhu Yan, Mu Gui, Li Jing
School of Mathematics Kunming University, Kunming University, Kunming, Yunnan, China.
Department of Rehabilitation, Baoshan People's Hospital, Baoshan, Yunnan, China.
Front Med (Lausanne). 2025 Jun 16;12:1591832. doi: 10.3389/fmed.2025.1591832. eCollection 2025.
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.
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.
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.
糖尿病视网膜病变（DR）筛查在早期检测上面临严峻挑战，因为其发病时无症状，且传统预测模型存在局限性。虽然现有研究主要集中在基于图像的人工智能诊断上，但迫切需要利用结构化临床数据进行准确的风险预测。本研究的目的是通过五种传统统计模型和深度学习模型开发、比较并验证糖尿病患者视网膜病变预测模型。
基于国家人口健康科学数据中心糖尿病并发症数据集的3000个数据点，使用SPSS软件对糖尿病患者和糖尿病合并视网膜病变患者的特征差异进行统计分析。使用五种传统机器学习模型和一个基于深度神经网络（DNN）的模型训练模型，以评估糖尿病患者的视网膜病变。
基于深度学习的预测模型在所有数据集上均优于传统机器学习模型，即逻辑回归、决策树、朴素贝叶斯、随机森林和支持向量机，在预测糖尿病患者视网膜病变方面表现更好（准确率分别为0.778对0.753、0.630、0.718、0.758、0.776；F1分数分别为0.776对0.751、0.602、0.724、0.755、0.776；AUC分别为0.833对0.822、0.631、0.769、0.829、0.831）。为提高深度学习模型的可解释性，采用SHAP分析评估特征重要性，并深入了解视网膜病变预测的关键驱动因素。
深度学习模型可以准确预测糖尿病患者的视网膜病变。本研究结果可用于通过为高危患者分配资源进行预防和监测。
