Development and validation of a machine learning model for predicting occupational stress among primary and secondary school teachers

Dados Bibliográficos

ID22070525
AutoresJinyu Wang (0000-0001-5689-6174, Lanzhou University), Xinran Zhang (0009-0008-0291-4486, Lanzhou University Second Hospital), Sheng Li (0000-0002-9662-9385, Taizhou Second People's Hospital, autor correspondente)
Ano2026
Volume14
Páginas1767113-1767113
Data de publicação2026-04-08
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1767113
PMID42027941
OpenAlexW7151994381
IdiomaEN
Referências citadas27

Background: To address the limitations of traditional linear tools in predicting teacher occupational stress, this study aimed to develop and validate machine learning models using easily obtainable, self-reported data. Method: A cross-sectional study of 2,832 in-service teachers in Lanzhou, China, was conducted. The presence of occupational stress, defined by the Core Occupational Stress Scale, was modeled using sociodemographic, work-related, and lifestyle factors. The dataset was partitioned into training (80%) and validation (20%) sets to compare six machine learning models: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), a Backpropagation Neural Network, Elastic Net, Logistic Regression, and a Support Vector Machine. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve, accuracy, F1-score, and Decision Curve Analysis. The optimal model was interpreted using the SHapley Additive exPlanations method. Results: The prevalence of occupational stress was 33.3%. On the validation set, the Extreme Gradient Boosting model demonstrated the best performance, with an Area Under the Curve of 0.620, an accuracy of 0.603, and an F1-score of 0.682. Decision Curve Analysis confirmed this model provided the highest net benefit. The LightGBM and Neural Network models exhibited significant overfitting. SHapley Additive exPlanations analysis identified weekly exercise time, sex, and age as the most influential predictors. A user-friendly, web-based tool was developed from the final model. Conclusion: Machine learning, particularly the Extreme Gradient Boosting algorithm, can effectively predict occupational stress in teachers. This approach offers a promising tool for early identification, enabling targeted interventions

Artificial neural network · Gradient boosting · Logistic regression · Occupational stress · Predictive modelling · Receiver operating characteristic · Support vector machine · Stress Responses and Cortisol · Technostress in Professional Settings · Workplace Health and Well-being