Interpretable machine learning models for predicting childhood myopia from school-based screening data
Abstract This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 children aged 5–12 years were included in the study. The participants were exposed to non-cycloplegic and cycloplegic refraction tests, along w...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05021-0 |
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