AUTHOR=An Hengqing , Xu Lei , Liu Yuanyuan , Ma Dongsheng , Zhang Dajun , Tao Ning 

TITLE=Study on a Bayes evaluation of the working ability of petroleum workers in the Karamay region, Xinjiang, China

JOURNAL=Frontiers in Psychology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.1011137

DOI=10.3389/fpsyg.2022.1011137

ISSN=1664-1078

ABSTRACT=Objectives
Use Bayes statistical methods to analyze the factors related to the working ability of petroleum workers in China and establish a predictive model for prediction so as to provide a reference for improving the working ability of petroleum workers.
Results
(1)The unsupervised Bayesian network shows that there is a direct relationship between shoulder and neck musculoskeletal diseases, anxiety, working age, and work ability, (2) The supervised Bayesian network shows that anxiety, depression, shoulder and neck musculoskeletal diseases (Musculoskeletal Disorders, MSDs), low back musculoskeletal disorders (Musculoskeletal Disorders, MSDs), working years, age, occupational stress, and hypertension are relatively important factors that affect work ability. Other factors have a relative impact on work ability but are less important. (3) The explanatory tree model of work ability shows that the joint probability of anxiety (Yes), depression (Yes), shoulder and neck MSDs (Yes), and waist and back MSDs (Yes) occurring simultaneously is 5.33%. At this time, work ability The high probability distribution accounts for 12.37%, the medium work ability probability distribution accounts for 61.16%, and the low work ability probability distribution accounts for 26.46%. The joint probability of having no anxiety, no depression, no shoulder and neck MSDs, and no waist and back MSDs at the same time is 4.53%, the probability distribution of high working ability accounts for 94.41%, the probability distribution of medium working ability accounts for 5.24%, and the probability distribution of low working ability accounts for 0.34%. (4) The Tree Augmented Naïve Bayes prediction model shows that after ten-fold cross-validation, the accuracy of the model is 86.68%, the reliability is 82.23%, and the AUC is 72.49%.
Conclusions
Anxiety, depression, shoulder and neck MSDs, waist and back MSDs, and length of service are important influencing factors of work ability. The Tree Augmented Naïve Bayes prediction model has general performance in predicting workers' work ability, and the Bayesian model needs to be deepened in subsequent research and a more appropriate forecasting method should be chosen.