  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.
