Supplementary file 1_Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models.docx
Version 1ObjectivesTo investigate the role of retinal characteristics for high myopia (HM) prediction based on multi-machine learning (ML) and to provide an interpretable framework for the results.MethodsA total of 2981 patients (2981 eyes) were included, comprising 1191 HM eyes and 1790 non-HM eyes. A deep semantic segmentation network was used to quantify retinal structural parameters. Five ML algorithms were evaluated to develop predictive models, and SHapley Additive exPlanations method was applied t
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ObjectivesTo investigate the role of retinal characteristics for high myopia (HM) prediction based on multi-machine learning (ML) and to provide an interpretable framework for the results.MethodsA total of 2981 patients (2981 eyes) were included, comprising 1191 HM eyes and 1790 non-HM eyes. A deep semantic segmentation network was used to quantify retinal structural parameters. Five ML algorithms were evaluated to develop predictive models, and SHapley Additive exPlanations method was applied t