Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning
10.1016/j.shaw.2025.01.003
- Author:
Saemi JUNG
1
;
Bogeum KIM
;
Yoon-Ji KIM
;
Eun-Soo LEE
;
Dongmug KANG
;
Youngki KIM
Author Information
1. Department of Occupational and Environmental Medicine, Pusan National University Yangsan Hospital, Republic of Korea
- Publication Type:Original article
- From:Safety and Health at Work
2025;16(1):113-121
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:This study aimed to develop prediction models for the work-relatedness of shoulder diseases through machine learning algorithms.
Methods:The dataset comprised 7,270 cases of 8,302 individuals who applied for occupational diseases and received the final approval decision from the Korea Workers' Compensation and Welfare Service's Disease Evaluation Committee, which is related to shoulder musculoskeletal disorders between January 2020 and December 2021. In this study, demographic analysis and difference of approval rate by shoulder diseases were performed. Additionally, machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, and the XGBoost, were utilized to construct prediction models for work-relatedness assessment.
Results:The performance of each model was evaluated. XGBoost showed an accuracy of 81.64% and an area under the curve of 0.73, and random forest showed an accuracy of 84.46% and an area under the curve of 0.73. Key factors influencing work-relatedness assessment were employment period, physical burden score, gender, and age.
Conclusion:The application of various machine learning techniques showed high performance score, representing that it would be helpful to reduce the differences in judgment between occupational environment medicine physicians.
