Classifying the risk of work related low back disorders due to manual material handling tasks
Expert Systems with Applications: An International Journal
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Work related low back disorders (LBDs) due to manual material handling (MMH) tasks have long been recognized as one of the main occupational disabling injury that affects the quality of life of the industrial working population in the U.S. One of the efforts to comprehend the nature and phenomenon of LBDs due to MMH tasks was undertaken by Marras [18]. Based on multiple experiments they created a seminal data set and used it to build logistic regression models to identify significant variables and classify manual lifting tasks into high risk and low risk with respect to LBDs. Since then a number of studies have used the same data set to build and test various classification models. This paper analyzes the previous studies and employs the same data set to build and test seven classification methods. Though the performances of our best models are better than those reported in National Institute for Occupational Health and Safety (NIOHS) Guides and two of our previous studies, they are generally less optimistic than those reported in several other studies, this paper proposes a more systematic and reliable approach to creating and validating classifiers to distinguish between low and high risk manual lifting jobs that contribute to LBDs.