Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine
This study aims to investigate the prevalence and risk factors of musculoskeletal disorders (MSDs) among South Korean male office workers and to introduce a robust predictive model using the Robust and Sparse Twin Support Vector Machine (RSTSVM). A cross-sectional survey was conducted among male...
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| Format: | Article |
| Language: | English |
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MRE Press
2024-12-01
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| Series: | Journal of Men's Health |
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| Online Access: | https://oss.jomh.org/files/article/20241230-444/pdf/JOMH2024071101.pdf |
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| _version_ | 1841560999998521344 |
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| author | Haewon Byeon |
| author_facet | Haewon Byeon |
| author_sort | Haewon Byeon |
| collection | DOAJ |
| description | This study aims to investigate the prevalence and risk factors of
musculoskeletal disorders (MSDs) among South Korean male office workers and to
introduce a robust predictive model using the Robust and Sparse Twin Support
Vector Machine (RSTSVM). A cross-sectional survey was conducted among male office
workers in South Korea to assess the prevalence of MSDs and identify associated
risk factors. Data on ergonomic and psychosocial factors were collected and
analyzed. The RSTSVM model was developed and compared with traditional machine
learning models, including Support Vector Machine (SVM) and Gradient Boosting
Machine (GBM), to predict the risk of MSDs. The analysis revealed a high
prevalence of MSDs among the surveyed office workers, attributed to factors such
as prolonged sitting, repetitive hand/arm movements, standing posture and
carrying heavy objects. Prolonged static postures were significantly linked to
lower back pain and other musculoskeletal issues. Poor workstation ergonomics and
psychosocial stressors, such as high job demands and low job control, were also
identified as significant predictors of MSDs. The RSTSVM model demonstrated
superior performance in predicting MSDs, with an Area under the Receiver
Operating Characteristic Curve (AUC-ROC) value of 0.84, effectively managing
high-dimensional data and maintaining robustness against outliers and noise.
Furthermore, the RSTSVM model provided enhanced interpretability, making it
easier to identify and understand key risk factors compared to traditional
models. The study underscores the critical need for multifaceted intervention
strategies to address the ergonomic and psychosocial risk factors associated with
MSDs among office workers. Future research should focus on longitudinal studies
to establish causal relationships and evaluate the effectiveness of various
interventions across different occupational groups. |
| format | Article |
| id | doaj-art-d1ddb4a325c743fba39b1c65d5b9fa6f |
| institution | Kabale University |
| issn | 1875-6867 1875-6859 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MRE Press |
| record_format | Article |
| series | Journal of Men's Health |
| spelling | doaj-art-d1ddb4a325c743fba39b1c65d5b9fa6f2025-01-03T08:41:59ZengMRE PressJournal of Men's Health1875-68671875-68592024-12-012012414910.22514/jomh.2024.199S1875-6867(24)00307-5Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machineHaewon Byeon0Department of Digital Anti-Aging Healthcare (BK21), Inje University, 50834 Gimhae, Republic of KoreaThis study aims to investigate the prevalence and risk factors of musculoskeletal disorders (MSDs) among South Korean male office workers and to introduce a robust predictive model using the Robust and Sparse Twin Support Vector Machine (RSTSVM). A cross-sectional survey was conducted among male office workers in South Korea to assess the prevalence of MSDs and identify associated risk factors. Data on ergonomic and psychosocial factors were collected and analyzed. The RSTSVM model was developed and compared with traditional machine learning models, including Support Vector Machine (SVM) and Gradient Boosting Machine (GBM), to predict the risk of MSDs. The analysis revealed a high prevalence of MSDs among the surveyed office workers, attributed to factors such as prolonged sitting, repetitive hand/arm movements, standing posture and carrying heavy objects. Prolonged static postures were significantly linked to lower back pain and other musculoskeletal issues. Poor workstation ergonomics and psychosocial stressors, such as high job demands and low job control, were also identified as significant predictors of MSDs. The RSTSVM model demonstrated superior performance in predicting MSDs, with an Area under the Receiver Operating Characteristic Curve (AUC-ROC) value of 0.84, effectively managing high-dimensional data and maintaining robustness against outliers and noise. Furthermore, the RSTSVM model provided enhanced interpretability, making it easier to identify and understand key risk factors compared to traditional models. The study underscores the critical need for multifaceted intervention strategies to address the ergonomic and psychosocial risk factors associated with MSDs among office workers. Future research should focus on longitudinal studies to establish causal relationships and evaluate the effectiveness of various interventions across different occupational groups.https://oss.jomh.org/files/article/20241230-444/pdf/JOMH2024071101.pdfmusculoskeletal disorderspsychosocial factorsoffice workersrstsvm model |
| spellingShingle | Haewon Byeon Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine Journal of Men's Health musculoskeletal disorders psychosocial factors office workers rstsvm model |
| title | Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine |
| title_full | Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine |
| title_fullStr | Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine |
| title_full_unstemmed | Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine |
| title_short | Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine |
| title_sort | predicting occupational musculoskeletal disorders in south korean male office workers using a robust and sparse twin support vector machine |
| topic | musculoskeletal disorders psychosocial factors office workers rstsvm model |
| url | https://oss.jomh.org/files/article/20241230-444/pdf/JOMH2024071101.pdf |
| work_keys_str_mv | AT haewonbyeon predictingoccupationalmusculoskeletaldisordersinsouthkoreanmaleofficeworkersusingarobustandsparsetwinsupportvectormachine |