Predicting the Risk of Burnout Syndrome Using Korean Occupational
Stress Scale (KOSS): A Machine Learning Approach
Dublin Core
Title
Predicting the Risk of Burnout Syndrome Using Korean Occupational
Stress Scale (KOSS): A Machine Learning Approach
Stress Scale (KOSS): A Machine Learning Approach
Subject
Burnout syndrome
Machine learning
Mental health
Occupational stress
SHAP
Machine learning
Mental health
Occupational stress
SHAP
Description
Changes in the workplace have increased occupational stress, leading to health issues such
as burnout syndrome (BOS), which results from poorly managed chronic workplace stress. The Korean
Burnout Syndrome Scale (KBOSS) has been used to diagnose these issues, but its stigma and decreased
compliance with application have limited its widespread use. This study aimed to develop machine
learning models to predict BOS risk from occupational stress factors and identify these influential
factors.
Methods: Using a dataset of 1,205 individuals across 40 companies, we evaluated the predictive performance of five machine learning algorithms. Each model was optimized via resampling and 5-fold grid
search cross-validation and evaluated using an ROC-AUC, balanced accuracy, overall accuracy, and F1
score. SHAP was used to quantify the contribution of each feature to the prediction, identifying key
occupational stress factors.
Results: All five models demonstrated strong predictive performance, with random forest achieving the
most balanced results across the evaluation metrics, including a ROC-AUC of 0.904. SHAP analysis
identified “Job instability” and “Lack of reward” as the most substantial BOS risk factors; “Relationship
conflict” and “Organizational system” also played important roles. Moreover, the relationship between
the SHAP values and feature values revealed critical transition points between “Agree” and “Disagree”
responses for each KOSS factor.
Conclusion: This study demonstrated that machine learning can effectively predict BOS risk based on
occupational stress factors. By enabling the early identification of at-risk employees, this approach
improves cost efficiency and offers a scalable solution for BOS risk assessment and intervention.
as burnout syndrome (BOS), which results from poorly managed chronic workplace stress. The Korean
Burnout Syndrome Scale (KBOSS) has been used to diagnose these issues, but its stigma and decreased
compliance with application have limited its widespread use. This study aimed to develop machine
learning models to predict BOS risk from occupational stress factors and identify these influential
factors.
Methods: Using a dataset of 1,205 individuals across 40 companies, we evaluated the predictive performance of five machine learning algorithms. Each model was optimized via resampling and 5-fold grid
search cross-validation and evaluated using an ROC-AUC, balanced accuracy, overall accuracy, and F1
score. SHAP was used to quantify the contribution of each feature to the prediction, identifying key
occupational stress factors.
Results: All five models demonstrated strong predictive performance, with random forest achieving the
most balanced results across the evaluation metrics, including a ROC-AUC of 0.904. SHAP analysis
identified “Job instability” and “Lack of reward” as the most substantial BOS risk factors; “Relationship
conflict” and “Organizational system” also played important roles. Moreover, the relationship between
the SHAP values and feature values revealed critical transition points between “Agree” and “Disagree”
responses for each KOSS factor.
Conclusion: This study demonstrated that machine learning can effectively predict BOS risk based on
occupational stress factors. By enabling the early identification of at-risk employees, this approach
improves cost efficiency and offers a scalable solution for BOS risk assessment and intervention.
Creator
Hyeonju Jeong 1
, Seong-Cheol Yang 2
, Shin-Goo Park 2,3
, Inho Hong 1,*,#,
Hyung Doo Kim 4,5,*
, Seong-Cheol Yang 2
, Shin-Goo Park 2,3
, Inho Hong 1,*,#,
Hyung Doo Kim 4,5,*
Source
https://pdf.sciencedirectassets.com/287282/1-s2.0-S2093791125X00056/1-s2.0-S2093791125000733/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEND%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIFXFAaETqVmiTqRG85wNQnlxoNr1PlB8ZnRZ5PdmertpAiABLA9XHpQ3OmXczfR0kVHijGZVQUiEryrpGngiTZLmAiq8BQiZ%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMxAH14L3ZvdOW4%2FPrKpAFEMBlcwmaMGZlxPyKskCgNkqauR%2B9dJiHH47aDZ528CiRX8RVArZ%2BYXdygLewTSjq3Z3JzZbtDDErJUb0G%2BeG%2FeL9rZ7STWt20vxHZ8fXK%2BByBRepTeU8j%2FW3hLm18L7fmt1C%2FJ5n6BJJSYdlUokj6PvaJGaCsrVzMoq%2FZ%2Fiv2JLfD3GVHNYie4ATgRVvzVIb7IamM3euvZHe6%2B3Br32mql6MgMvNDqrTVcfaRGEcBFwMODgLCpWWCKpR4SrdtDokwYDbWYFlu%2F5eG7xZvyWP5LAF1zLlmSW5m%2BRSmohw%2FoM7hi9bSEfEtnwcMmy4eZZMG7A8jtfBzYI4ZnOtCHy3YpRK08ParrE7OqqT%2BFBNag6%2F1hsOgPOe3%2Fmb%2FeU%2BV%2BXIqR44Vgm%2BUXdSbS7d2q1mA2f0283bK6w0TR9sJHUl4caBX3kqPHAK44exWbZnzd6rpTJO6L0ceyTdcw5Ag%2FfFjA4lZKqhwXVuL4iqVAzictp5TPS%2F%2FGOGJ%2FmcQd3jgW4UMIlL32olNl1XWSCwLbXg8uIU40AnViVTT3DKy7ZZ1mlp97fB40KK1wJDyY22EIOnMx4bsKZBzj2ykliWmaIxF48ItyM9itPLXUZGmCXoU7TGT3GBzCpe4odiQNB29YvE376ybZTW29chTFuu6cddhCVwnUaCfcitd7xeRk9RQOlSZx2jL7bcwF%2Fs5BZyWFDp%2FuR0LRqz%2FGw9MCFEt4O%2Fq6%2FB5Ngc5KX4YRb50l%2F33Oh9hJeSntCqk4IDUi2kq98ge7uTVPJcQG6SUrSv1mXhMcgiLtGDyf0DhzgkTtwMeUaalotvCfJkPu%2BTEI49OfIf2tce9w4toBcpZ6VTkqzuPN6PxhuxcImiavIZCUW9DCww0J6azQY6sgE0KAQbz8ZtnQM%2FWKsOKc9M8NlFXPNTPxbDqrMjtQOSlQ0RiBraVFm2Ah8K5EYhEJ73rD3LWooGdq2UKhz3xiL3TK2IZS6rhmanVFhFUxqTfBFdR6SEfC9LgY%2FncyX2Hz3WIagGiLi2TDusOD9rLhUn0TrGzZeHBedqy3CRJrQpThGe5iw1YaKzDb4mZLSNUyPD5O6%2FOlFaSxsm%2BIA%2F2IvQWN41RE%2FSFUZAXOYleH%2B6qvVy&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20260303T081322Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY375TXT2U%2F20260303%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7abcb188553cfcc134480b155eec606ba81fe18840fa96a22f74b71aa6c66af9&hash=864a2a5bf2ec1511d7acb7e10df390f9f199af5e5465cea2c5ee5a410ba162ae&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2093791125000733&tid=spdf-f7b5f813-8b9e-4dda-a56c-0ee19da275fa&sid=2ded17629244b949e60afc0450ecf4110ca9gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&rh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0b015e065155530257&rr=9d672e7829e66d12&cc=id
Publisher
1Graduate School of Data Science, Chonnam National University, Gwangju, Republic of Korea 2Department of Social and Preventive Medicine, Inha University College of Medicine, Incheon, Republic of Korea 3Department of Occupational and Environmental Medicine, Inha University Hospital, Incheon, Republic of Korea 4Department of Occupational and Environmental Medicine, Hanyang University Guri Hospital, Guri, Republic of Korea 5Department of Environmental Sciences, Seoul National University Graduate School of Public Health, Seoul, Republic of Korea
Date
6 September 2025
Contributor
Fajar Bagus Wijanarko
Format
pdf
Language
english
Type
text
Files
Citation
Hyeonju Jeong 1
, Seong-Cheol Yang 2
, Shin-Goo Park 2,3
, Inho Hong 1,*,#,
Hyung Doo Kim 4,5,*, “Predicting the Risk of Burnout Syndrome Using Korean Occupational
Stress Scale (KOSS): A Machine Learning Approach,” Repository Horizon University Indonesia, accessed April 11, 2026, https://repository.horizon.ac.id/items/show/12033.
Stress Scale (KOSS): A Machine Learning Approach,” Repository Horizon University Indonesia, accessed April 11, 2026, https://repository.horizon.ac.id/items/show/12033.