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.

ASSOCIATION BETWEEN BODY MASS INDEX AND DIABETES MELLITUS CONTROL CLASSIFICATION AMONG PATIENTS WITH TYPE 2 DIABETES MELLITUS: EVIDENCE FROM THE NATIONAL DIABETES REGISTRY OF MUAR DISTRICT HEALTH OFFICE, MALAYSIA, FROM JANUARY 2021 TO JULY 2023 Introduction: Type 2 diabetes mellitus (T2DM) is one of the most prevalent non-communicable diseases globally. This study aimed to determine the association between body mass index (BMI) and diabetes mellitus (DM) control among patients with T2DM. Methods: A retrospective study was conducted from October 2023 to June 2024 using secondary data from the National Diabetes Registry (NDR) of Muar District Health Office, Johor. Patients with T2DM registered in the NDR and audited from 2021 to July 2023 were included. The association between BMI and DM control was analysed using hierarchical multinomial logistic regression. Results: A total of 1955 patients were included in the study. The prevalence of good, intermediate and poor BMI control was 38.8% (95% Confident Interval (CI)=36.7, 41.0), 22.2% (95% CI=20.3, 24.0) and 39.0% (95% CI=36.7, 41.2), respectively. Most patients were older Malay women. There was an association between BMI and DM control unadjusted (P<0.001) and adjusted for several confounding factors using seven models (P=0.003–0.034). The R2 value also improved from 0.008 to 0.293. Conclusion: Among patients with T2DM, a higher BMI, the creatinine level and medications such as glucose-lowering drugs, ticlopidine, acetylsalicylic acid and statins are associated with DM control. However, as the study design does not allow for the assessment of causality or progression over time, the findings should be interpreted as descriptive associations rather than as evidence of cause-and effect relationships. Focus on medication compliance, healthcare providers’ role during medication consultation and stakeholders’ role in maintaining drug supplementation is needed.