{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:13:46Z","timestamp":1786115626306,"version":"build-2736575974"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were obtained from the Fujian Eye Study, comprising 8211 participants and 51 variables. After data preprocessing, five machine learning models\u2014Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree (DT), and Random Forest (RF)\u2014 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\u2014Factor Analysis (FA), Highly Variable Feature Selection (HVGS), and Spearman\u2019s rank correlation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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\u2014diabetes history, age, pulse pressure difference, left eye near visual acuity, and height\u2014provide 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1834380","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T04:25:29Z","timestamp":1785990329000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning models for risk prediction of diabetic retinopathy in Fujian eye study"],"prefix":"10.3389","volume":"17","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"Eye Institute and Affiliated Xiamen Eye Center of Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]},{"name":"Xiamen Clinical Research Center for Eye Diseases, Xiamen","place":["Fujian, China"]},{"name":"Xiamen Key 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