Factors associated with work-related musculoskeletal disorders using machine learning approaches: a systematic review
10.35371/aoem.2026.38.e10
- Author:
Muhammad Irfan MOHD SALLEHHUDIN
1
;
Siti Munira YASIN
;
Mohamad Rodi ISA
;
Tajul Rosli RAZAK
;
Muhamad Syazni MOHAMAD ASRAFF
;
Nur Adilla CHE RAMELI
;
Muhammad Muaz SHAHRIMAN-TERUNA
;
Muhammad Muzzammil MOHAMAD SALLEH
;
Mohamad Zuhair MOHAMED YUSOFF
;
Muhammad Hariz AMMAR KHEBIR
Author Information
1. Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Jalan Hospital, Sungai Buloh, Malaysia
- Publication Type:Review
- From:Annals of Occupational and Environmental Medicine
2026;38(1):e10-
- CountryRepublic of Korea
- Language:0
-
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.
