What type of study is this?

This is a Cross-Sectional Study study.

What question did this study set out to answer?

To evaluate the role of modifiable risk factors and machine learning in predicting myopia among children and adolescents.

November 13, 2025Frontiers in MedicineOpen Access

Optimizing myopia prediction in children and adolescents using machine learning: a multi-factorial risk assessment model

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Authors

YXYue Xi WZWei Zhu WYWenjing Yan

Discussion

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Overview

Cross-sectional analysis identifies behavioral and genetic factors influencing myopia risk in children, suggesting machine learning models can predict outcomes effectively.

Key Points

To evaluate the role of modifiable risk factors and machine learning in predicting myopia among children and adolescents.
Cross-sectional survey conducted in schools in China
Data split into training and testing sets
LASSO regression and multivariate logistic regression used for predictor identification
Ten machine learning algorithms tested for prediction accuracy
Model performance evaluated using accuracy, F1 score, and AUC.
Myopia prevalence was 25.12% among 2,086 children and adolescents
LightGBM achieved the best predictive performance (AUC = 0.738)
Key risk factors included parental myopia, physical activity, and only-child status.

Cite This Study

Xi et al. (2025) studied this question.

synapsesocial.com/papers/692523bbc0ce034ddc354926 https://doi.org/10.3389/fmed.2025.1672432

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November 13, 2025Frontiers in MedicineOpen Access

Optimizing myopia prediction in children and adolescents using machine learning: a multi-factorial risk assessment model

View Full Paper

Ask AI

Bookmark

Authors

YXYue Xi WZWei Zhu WYWenjing Yan

Discussion

Member takes

Overview

Cross-sectional analysis identifies behavioral and genetic factors influencing myopia risk in children, suggesting machine learning models can predict outcomes effectively.

Key Points

To evaluate the role of modifiable risk factors and machine learning in predicting myopia among children and adolescents.
Cross-sectional survey conducted in schools in China
Data split into training and testing sets
LASSO regression and multivariate logistic regression used for predictor identification
Ten machine learning algorithms tested for prediction accuracy
Model performance evaluated using accuracy, F1 score, and AUC.
Myopia prevalence was 25.12% among 2,086 children and adolescents
LightGBM achieved the best predictive performance (AUC = 0.738)
Key risk factors included parental myopia, physical activity, and only-child status.

Cite This Study

Xi et al. (2025) studied this question.

synapsesocial.com/papers/692523bbc0ce034ddc354926 https://doi.org/10.3389/fmed.2025.1672432

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