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- Review Factors associated with work-related musculoskeletal disorders using machine learning approaches: a systematic review
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Muhammad Irfan Mohd Sallehhuddin1
, Siti Munira Yasin1,*, Mohamad Rodi Isa1, Tajul Rosli Razak2, Muhamad Syazni Mohamad Asraff1, Nur Adilla Che Rameli1, Muhammad Muaz Shahriman-Teruna1, Muhammad Muzzammil Mohamad Salleh1, Mohamad Zuhair Mohamed Yusoff1, Muhammad Hariz Ammar Khebir1 -
DOI: https://doi.org/10.35371/aoem.2026.38.e10 [Accepted]
Published online: March 19, 2026
1Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Jalan Hospital, Sungai Buloh, Malaysia
2Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia
Received: 19 December 2025 • Revised: 6 March 2026 • Accepted: 12 March 2026
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Abstract
Background
Work-related musculoskeletal disorders (WRMSDs) remain a major cause of occupational disability and productivity loss worldwide. Traditional statistical methods have identified numerous associated factors; however, they often struggle to capture complex non-linear relationships and interactions across multiple domains of risk. Machine learning (ML) offers an alternative analytical approach for modelling such multidimensional relationships.
Methods
Following the PRISMA 2020 guidelines (PROSPERO: CRD420250605234), literature searches were conducted in Web of Science, Scopus, and PubMed for studies published between 2020 and 2025. Eligible studies applied ML methods to identify factors associated with WRMSDs using cross-sectional study designs. Included studies were appraised using the Joanna Briggs Institute Critical Appraisal Checklist for analytical cross-sectional studies.
Results
Ten studies met the inclusion criteria, representing workers from healthcare, transport, manufacturing, and service sectors across Asia, Africa, and Europe. Frequently applied ML algorithms included random forest, support vector machine, and artificial neural networks, demonstrating strong internal discriminative performance (area under the receiver operating characteristic curve: 0.80–0.99), although the absence of external validation in several studies suggests a potential risk of overfitting. Commonly identified factors included age, sex, awkward posture, vibration exposure, prolonged working hours, stress, and burnout. Psychosocial factors, including post-traumatic stress disorder, job stress, and depression, were ranked among the most influential predictors within ML models.
Conclusions
ML models demonstrate strong capability in discriminating WRMSDs risk and identifying multidimensional risk factors compared with traditional statistical approaches. These models highlight complex interrelationships between ergonomic and psychosocial exposures. Future research should incorporate external validation, objective exposure measurements, and standardized ML reporting frameworks to enhance methodological transparency and generalizability.
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