International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Universal approximation using radial-basis-function networks
Neural Computation
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Machine Learning
Human Factors in Ergonomics & Manufacturing
NEFCLASS based extraction of fuzzy rules and classification of risks of low back disorders
Expert Systems with Applications: An International Journal
Ant colony optimization approach for classification of occupational low back disorder risks
Human Factors in Ergonomics & Manufacturing
Data Mining: Concepts, Models, Methods, and Algorithms
Data Mining: Concepts, Models, Methods, and Algorithms
Predicting the Risk of Low Back Disorders due to Manual Handling Tasks
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of Key Variables Using Fuzzy Average With Fuzzy Cluster Distribution
IEEE Transactions on Fuzzy Systems
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Work related low back disorders (LBDs) due to manual lifting tasks (MLTs) 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. There have been a number of intensive research efforts devoted to understanding the phenomena of LBDs and building classification models that could effectively distinguish between high risk and low risk MLTs that contribute to LBDs. As of today, however, such models and the occupational exposure limits of different risk factors causing LBDs as well as the guidelines preventing them have not yet been fully proposed. One of the first efforts to comprehend the nature and phenomenon of LBDs was undertaken by Marras et al. (1993). They created a seminal data set and used it to build logistic regression (LR) models to identify significant variables and classify MLTs 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 classifiers to detect the likelihood of LBDs due to manual material handling jobs. This paper summarizes and critiques the previous studies. It also employs this data set to build and test seven classification models, two of which have not been applied in this context yet. The parameters of the models have been calibrated for the best performance, and the models were constructed and validated on the full set and the reduced set of features. 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 systematic and more reliable approach to creating and validating classifiers to distinguish between low and high risk MLTs that contribute to LBDs.