Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning.

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 ass

SUBMITTER: Jung S 

PROVIDER: S-EPMC11959547 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature