A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder

Abstract

In recent times, musculoskeletal disorders (MSD) represent one of the most common and expensive occupational health problems in both developed and developing countries. Work-related musculoskeletal disorders (WRMSD) are impairments that are mostly caused by the workplace and immediate environment. A two-step predictive model is introduced here using KNN and Decision tree machine learning algorithms. This model for predicting WRMSD enables for early detection and correction of upper and lower back disorders, carpal tunnel syndrome and other WRMSD disorders associated with office workers. Key informant interview technique, observation of previous methods, online repository and published related works were used in data gathering. In training the model, 80% of the dataset was used while 20% was used for testing the model to prevent overfitting using python programming language. JavaScript, Hypertext preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Stylesheet (CSS) and MySQL were also used to develop the front and backend of the application. The result revealed that the proposed model had 90.44% accuracy, 92.71% Recall (sensitivity), 97.16% precision, and 94.88% F1-Score. The proposed model, however, makes it easy for multiple classifications in other to predict both present and future risk of WRMSD. Performance is estimated to have high accuracy, recall, precision and f1 score in comparison to other existing algorithms.