OCCUPATION AND HEALTH ›› 2026, Vol. 42 ›› Issue (18): 2460-2466.

• Treatise——Occupational Health and Occupational Diseases • Previous Articles     Next Articles

Analysis of anxiety status and influencing factors among 873 automobile manufacturing workers

YANG Jie, ZHANG Jinwei, ZHANG Yuxia, ZHAO Yuan, WANG Zhi, ZHOU Hailin()   

  1. Guangzhou Twelfth People's HospitalGuangzhouGuangdong 510620, China
  • Received:2025-10-21 Revised:2026-02-11 Online:2026-09-15 Published:2026-08-28
  • Contact: ZHOU Hailin

Abstract:

Objective To apply machine learning to analyze the anxiety status and influencing factors of workers in Guangzhou's automobile manufacturing industry,and provide data reference for improving the physical and mental health status of automobile manufacturing workers and promoting sustainable development of the manufacturing industry. Methods From August 2022 to September 2023,a simple random sampling was used to select 8 sample units in the automobile manufacturing industry of Guangzhou,and a total of 873 front-line workers were surveyed. Respondents were randomly divided into a training group(n=611) and a validation group(n=262) at a ratio of 7∶3,and feature screening was performed using univariate Logistic regression,Boruta regression,Lasso regression and recursive feature elimination(RFE).Six kinds of machine learning models were established,and the performance of different models was compared by receiver operating characteristic(ROC) area under the curve(AUC),calibration curve and decision curve analysis(DCA).Shapley additive explanation(SHAP) algorithm was used to evaluate the factors affecting anxiety. Results Among the 873 respondents,284(32.5%) experienced anxiety.After screening for characteristic indicators,eight characteristic variables including gender,marital status,length of employment,nature of the company,level of health and legal knowledge,level of healthy work Methods and behaviors,occupational stress,and sleep status were selected and incorporated into six machine learning models to construct predictive models.Among them,the LightGBM model had the highest AUC of 0.705,while the AUCs of the other five models,from high to low,were 0.683,0.667,0.634,0.630,and 0.601.The LightGBM model also demonstrated greater clinical decision net benefit in the DCA curve,indicating the best overall predictive performance. The SHAP algorithm analysis revealed that having sleep disorders,experiencing occupational stress,working in a foreign-funded enterprise,and having a longer tenure in the current position would increase the risk of anxiety,while high levels of health and legal knowledge and healthy work Methods and behaviors would reduce the risk of anxiety. Conclusion The anxiety of workers in Guangzhou's automotive manufacturing industry warrants attention.Enterprises can mitigate anxiety risks by optimizing job assignments,rest periods,and working conditions,as well as enhancing occupational health education.Additionally,the LightGBM model can be utilized to identify risk factors for anxiety,enabling early intervention.

Key words: Automobile manufacturing industry, Worker, Anxiety, Influencing factors, Machine learning models, Shapley additive explanation algorithm

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