Development of machine learning identification models for relatively high risk of social-emotional problems among school-aged children in China
10.16835/j.cnki.1000-9817.2026267
- VernacularTitle:中国学龄儿童社会情绪相对高风险机器学习识别模型的构建
- Author:
WANG Ying, QIAN Jinwei, XU Hua, XU Peng, XIA Senlin
1
Author Information
1. Fifth School of Clinical Medicine, Zhejiang Chinese Medical University, Huzhou 313000,Zhejiang Province, China
- Publication Type:Journal Article
- Keywords:
Emotions;
Mental health;
Models,statistical;
Child
- From:
Chinese Journal of School Health
2026;47(9):1298-1302
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop machine learning identification models for the relatively high risk of social-emotional problems among Chinese school-aged children, and compare internal validation performances across different algorithms, so as to provide reference for preliminary identification of relatively high risk in school-aged children s social-emotional problems.
Methods:Based on the 2022 proxy-reported data for children from the China Family Panel Studies (CFPS), a total of 3 575 children aged 6-15 years from 2 709 families who completed the survey between May and December 2022 were included. Relatively high risk of social-emotional problems was defined using a composite score constructed from 12 social emotional related items in the CFPS 2022 general questionnaire. Data were split approximately 7∶3 (modeling group: 2 503, validation group: 1 072) by household identifier (ID) to ensure all children from the same family were assigned to the same dataset. Candidate variable selection, missing value imputation parameter fitting, least absolute shrinkage and selection operator (LASSO) feature screening, model training, and hyperparameter tuning were performed within the development set, while internal validation was conducted in the validation set. Weighted AUC, Brier score, sensitivity, specificity, calibration curves, and decision curves were compared across Logistic regression, random forest (RF), gradient boosting decision trees (GBDT), support vector machines (SVM), and extreme gradient boosting (XGBoost). The Shapley additive explanations (SHAP) method was used for exploratory interpretation of the RF model, which achieved the highest AUC value in the validation set.
Results:A total of 941 (26.3%) were classified as having relatively high risk of social emotional problems. A total of 659 and 282 high risk cases were in the modeling group and validation group, respectively, with no cross set split of households. LASSO retained 10 features. In the validation set, the RF model achieved the highest AUC of 0.672 (95% CI =0.624-0.717) and a Brier score of 0.184. The Logistic regression model achieved a validation AUC of 0.670 (95% CI =0.624-0.716). The incremental AUC gain of RF over Logistic regression was 0.002 (family cluster paired bootstrap 95% CI =-0.016 to 0.020, P =0.856). Following household ID splitting, all models showed moderate internal validation performance. Although RF achieved the highest numerical value, its incremental gain over Logistic regression was limited.
Conclusion:The model can serve as a reference for preliminary identification research on the relatively high risk of social emotional problems in school aged children, though its stability and applicability require further validation in external samples.
