AUTHOR=Huang Zihan , Gong Di , Tang Cuicui , Wang Jinghui , Zhang Chenchen , Dang Kuanrong , Chai Xiaoyan , Wang Jiantao , Yan Zhichao 

TITLE=A risk prediction model for neovascular glaucoma secondary to proliferative diabetic retinopathy based on Boruta feature selection and random forest

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1604832

DOI=10.3389/fcell.2025.1604832

ISSN=2296-634X

ABSTRACT=BackgroundNeovascular glaucoma (NVG) is one of the most severe complications of proliferative diabetic retinopathy (PDR), carrying a high risk of blindness. Establishing an effective risk prediction model can assist clinicians in early identification of high-risk patients and implementing personalized interventions to reduce the incidence of vision impairment. This study aimed to develop and evaluate a risk prediction model for NVG in PDR patients based on the Boruta feature selection method and random forest algorithm to improve clinical predictive performance.MethodsThis retrospective study included 365 PDR patients treated at Shenzhen Eye Hospital between January 2019 and December 2024, comprising 269 controls (non-NVG) and 96 cases (NVG). The Boruta feature selection method was employed to identify key features associated with NVG development in PDR. A risk prediction model was then constructed using the random forest algorithm. Model performance was evaluated based on accuracy, sensitivity, specificity, and area under the curve (AUC). Additionally, calibration curves and decision curve analysis (DCA) were used to assess clinical utility. All data analyses and modeling were performed in R (version 4.2.3).ResultsThe Boruta algorithm selected 12 significant predictive features. The random forest-based model achieved an accuracy of 90.74%, sensitivity of 82.14%, specificity of 93.75%, and an AUC of 0.87, demonstrating strong predictive performance. Calibration curves indicated reliable prediction probabilities within the 0.4–0.8 range. Decision curve analysis revealed substantial clinical net benefit across threshold probabilities of 0.2–0.8.ConclusionThe Boruta-guided random forest model developed in this study exhibits excellent predictive performance and clinical applicability for assessing NVG risk in PDR patients.