Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students
- 1Chongqing Medical University
- 2Nan'an District Center for Disease Control and Prevention
Abstract
Introduction: Myopia and reduced vision among children and adolescents place a substantial burden on public health, particularly where eye-care resources are limited. We evaluated an interpretable questionnaire-based framework for modelling self-reported reduced uncorrected visual acuity.
Methods: The analysis included the first eligible record for each of 7,118 students from seven schools across three survey rounds. Sixteen demographic, familial and behavioural predictors were used to model an affirmative response to a question about uncorrected visual acuity below 5.0 in at least one eye. Internal-external cross-validation (IECV) comprised seven outer folds, each holding out one school. Preprocessing, model selection, probability calibration and Youden threshold estimation were confined to the training schools.
Results: The selected workflow achieved an area under the receiver operating characteristic curve (AUC) of 0.706 [95% confidence interval (CI) 0.694-0.718], a Brier score of 0.212, sensitivity of 0.730 and specificity of 0.575. The academic-year-only benchmark achieved an AUC of 0.611 and a Brier score of 0.233. Stage-specific AUCs ranged from 0.633 to 0.680, with substantial variation in sensitivity and specificity. Applying the same development procedure to the full cohort selected CatBoost. SHapley Additive exPlanations (SHAP) identified academic year, parental myopia and desk-chair adjustment as leading contributors to its predictions.
Discussion: Questionnaire responses provided predictive information about self-reported visual status beyond academic year, with moderate overall discrimination and variation across educational stages. These findings provide a basis for further evaluation of the framework in school eye-health research.
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BibTeX
@article{li2026interpretable,
title = {{Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students}},
author = {Yangzhou Li and Lin Zhang and Jikai Wang and Shihao Fu and Yalan Liu and Lingyan Yuan and Mengliang Ye},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1872000},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1872000/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students
AU - Yangzhou Li
AU - Lin Zhang
AU - Jikai Wang
AU - Shihao Fu
AU - Yalan Liu
AU - Lingyan Yuan
AU - Mengliang Ye
PY - 2026
DA - 2026/10/26
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1872000
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1872000/full
AB - Introduction: Myopia and reduced vision among children and adolescents place a substantial burden on public health, particularly where eye-care resources are limited. We evaluated an interpretable questionnaire-based framework for modelling self-reported reduced uncorrected visual acuity. Methods: The analysis included the first eligible record for each of 7,118 students from seven schools across three survey rounds. Sixteen demographic, familial and behavioural predictors were used to model an affirmative response to a question about uncorrected visual acuity below 5.0 in at least one eye. Internal-external cross-validation (IECV) comprised seven outer folds, each holding out one school. Preprocessing, model selection, probability calibration and Youden threshold estimation were confined to the training schools. Results: The selected workflow achieved an area under the receiver operating characteristic curve (AUC) of 0.706 [95% confidence interval (CI) 0.694-0.718], a Brier score of 0.212, sensitivity of 0.730 and specificity of 0.575. The academic-year-only benchmark achieved an AUC of 0.611 and a Brier score of 0.233. Stage-specific AUCs ranged from 0.633 to 0.680, with substantial variation in sensitivity and specificity. Applying the same development procedure to the full cohort selected CatBoost. SHapley Additive exPlanations (SHAP) identified academic year, parental myopia and desk-chair adjustment as leading contributors to its predictions. Discussion: Questionnaire responses provided predictive information about self-reported visual status beyond academic year, with moderate overall discrimination and variation across educational stages. These findings provide a basis for further evaluation of the framework in school eye-health research.
SN - 2296-2565
ER -