Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine - Haewon Byeon (Journal of Men's Health)
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.