Citation

Ahn G, Hur S, Jung MC. Int. J. Occup. Safety Ergonomics 2018; ePub(ePub): 1-12.

Affiliation

b Department of Industrial Engineering , Ajou University , Republic of Korea.

Copyright

(Copyright © 2018, Centralny Instytut Ochrony Pracy - Państwowy Instytut Badawczy, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/10803548.2018.1502131

PMID

30033819

Abstract

AIM: It is essential to understand the extent to which job characteristics impact work-related musculoskeletal disorders (WMSDs), and to calculate the probability that an employee will suffer from a musculoskeletal disorder given their working conditions. The objective of this research is to identify the relationships between work-related musculoskeletal disorders and working characteristics, by developing a Bayesian network (BN) model to calculate the probability that an employee suffers from a musculoskeletal disorder.

METHODS: A conceptual model was constructed based on a BN. This was then statistically tested and corrected to establish a BN model.

RESULTS: Experiments verified that the BN model achieves a better diagnostic performance than artificial neural network, support vector machine, and decision tree approaches, and is robust in diagnosing WMSDs given working characteristics.

CONCLUSION: It was verified that working characteristics, such as working hours and pace, impact the incidence rate of WMSDs, and a BN model was developed to probabilistically diagnose WMSDs.

Language: en

Keywords

Bayesian network; work-related musculoskeletal disorders; working characteristics