title: Application of artificial neural network models in occupational safety and health utilizing ordinal variables.
abstract: Safety professionals and practitioners are always searching for methods to accurately assess the association between exposures and possible occupational disorders or diseases and predict the outcome of any variable. Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used today. Artificial neural network (ANN) models are another method of predicting outcomes, which are gradually finding their way into the safety field. Limited studies have shown that they are capable of predicting outcomes more accurately than LR, but they have been tested either on continuous or on dichotomous variables or combinations of them. The objective of this research was to demonstrate that ANN models can perform better than LR models with data sets comprised of all ordinal variables, which has not been done so far. The data set used in this research was collected from construction workers using the Work Compatibility questionnaire. The data set contained only ordinal variables both as input (exposure) and as output (outcome) variables. LR models and ANN models were constructed using the same data set and the performance of all models was compared by using the log-likelihood ratio. The result of this study showed that ANN models performed significantly better than LR models with a data set of all ordinal variables as well as other types of variables such as dichotomous and continuous.
authors.affiliation: Department of the Built Environment, College of Technology, Indiana State University, Terre Haute, IN 47809, USA. farman.moayed@indstate.edu
authors.forename: Farman A
authors.initials: FA
authors.lastname: Moayed
authors.forename: Richard L
authors.initials: RL
authors.lastname: Shell
mesh_terms: D005544:Forecasting / Q000379:methods; D006801:Humans; D016571:Neural Networks, Computer; D009787:Occupational Medicine / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D017751:Safety Management / Q000379:methods*