Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models.

ABSTRACT:

Objectives

To 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.

Methods

A 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 to analyze feature contribution to the outcomes.

Results

The eXtreme Gradient Boosting achieved an accuracy of 0.81 (95% confidence interval [CI] 0.78-0.85), and an area under the receiver operating characteristic curve of 0.87 (95% CI 0.84-0.89), outperforming other models.

SUBMITTER: Zou H 

PROVIDER: S-EPMC12555007 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature