Bayesian network model to diagnose WMSDs with working characteristics. Issue 2 (2nd April 2020) - Ahn, Gilseung; Hur, Sun; Jung, Myung-Chul
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 WMSDs 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.