ArticleFrontiers in public health2026
SSR-stacking: a hybrid GNN-ensemble framework for high-myopia classification under simulated baseline-SE missingness.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The increasing burden of childhood myopia creates a need for screening models that remain informative when routinely collected clinical measurements are incomplete. We propose SSR-Stacking, a hybrid framework integrating Graph Neural Networks (GNN) with Ensemble Learning for classification of final-follow-up high-myopia status under simulated missingness of baseline spherical equivalent (SE). Unlike traditional models, we construct a Social-Environment Graph based on school-class affiliations to represent classroom-level relational structure. Our framework features a Self-Supervised Reconstruction (SSR) mechanism toreconstruct randomly masked baseline SE from neighboring peers and a Meta-Stacking layer to fuse GNN embeddings with diverse ML classifiers (XGBoost, SVM, RF). Evaluated on a longitudinal dataset (
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