Machine learning models for risk prediction of diabetic retinopathy in Fujian eye study.

BACKGROUND

Diabetic Retinopathy (DR) is among the most severe microvascular complications of diabetes, leading to visual impairment and diminished quality of life. This study developed and compared multiple machine learning models for DR risk prediction using population-based data from the Fujian Eye Study, aiming to identify the top five key predictors and establish a robust data-driven framework for early screening.

METHODS

Data were obtained from the Fujian Eye Study, comprising 8211 participants and 51 variables. After data preprocessing, five machine learning models-Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree (DT), and Random Forest (RF)- were trained and optimized via cross-validation and grid search. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, F1-score, and AUC. Feature importance was examined using SHAP (Shapley Additive Explanations) and validated through unsupervised and nonparametric approaches-Factor Analysis (FA), Highly Variable Feature Selection (HVGS), and Spearman's rank correlation.

RESULTS

Among the five models, SVC model achieved the highest performance (F1-score 92.83%, AUC 0.99). SHAP analysis identified the top five predictors of DR risk: history of diabetes, age, pulse pressure difference (PPG), near visual acuity of the left eye, and height. Cross-method comparison confirmed high feature stability across models, indicating robust predictor reproducibility.

CONCLUSION

This study successfully established and validated a machine learning-based framework for predicting diabetic retinopathy risk using data from the Fujian Eye Study. The support vector machine (SVC) model demonstrated superior predictive capability. The identified key risk factors-diabetes history, age, pulse pressure difference, left eye near visual acuity, and height-provide actionable insights for early stratification. These findings provide a reliable, data-driven tool for early DR screening, which can facilitate population-level risk stratification and inform personalized preventive interventions in clinical and public health settings.