A Natural Language Processing-Based Classification and Mode-Based Ranking of Musculoskeletal Disorder Risk Factors
This work addresses MSD prevention for occupational health by providing actionable insights, though it is incremental as it applies existing NLP methods to a specific domain.
The study tackled the classification and ranking of Musculoskeletal Disorder (MSD) risk factors using NLP models, achieving up to 100% accuracy with a sentence transformer and distance metrics, and identified 'Working posture' as the most severe factor.
This research delves into Musculoskeletal Disorder (MSD) risk factors, using a blend of Natural Language Processing (NLP) and mode-based ranking. The aim is to refine understanding, classification, and prioritization for focused prevention and treatment. Eight NLP models are evaluated, combining pre-trained transformers, cosine similarity, and distance metrics to categorize factors into personal, biomechanical, workplace, psychological, and organizational classes. BERT with cosine similarity achieves 28% accuracy; sentence transformer with Euclidean, Bray-Curtis, and Minkowski distances scores 100%. With 10-fold cross-validation, statistical tests ensure robust results. Survey data and mode-based ranking determine severity hierarchy, aligning with the literature. "Working posture" is the most severe, highlighting posture's role. Survey insights emphasize "Job insecurity," "Effort reward imbalance," and "Poor employee facility" as significant contributors. Rankings offer actionable insights for MSD prevention. The study suggests targeted interventions, workplace improvements, and future research directions. This integrated NLP and ranking approach enhances MSD comprehension and informs occupational health strategies.