@article{ref1,
title="Bayesian network model to diagnose WMSDs with working characteristics",
journal="International journal of occupational safety and ergonomics",
year="2018",
author="Ahn, Gilseung and Hur, Sun and Jung, Myung-Chul",
volume="ePub",
number="ePub",
pages="1-12",
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. <br><br>METHODS: A conceptual model was constructed based on a BN. This was then statistically tested and corrected to establish a BN model. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1080-3548",
doi="10.1080/10803548.2018.1502131",
url="http://dx.doi.org/10.1080/10803548.2018.1502131"
}