Related Experiment Video
Updated: May 16, 2025
Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
Published on: March 12, 2021
Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning
Saemi Jung1, Bogeum Kim2, Yoon-Ji Kim3
1Department of Occupational and Environmental Medicine, Pusan National University Yangsan Hospital, Republic of Korea.
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.
