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 outcome. Statistical analysis and logistic regression in particular are among the most popular tools being used by them. Artificial Neural Network (ANN) models are another method of predicting outcomes, which are gradually finding their way in the safety field. It has been shown that they are capable of predicting outcomes more accurately than logistic regression, but they are incapable of demonstrating the direct correlation between exposure variables and possible outcome variables. The first objective in this research was to demonstrate that Artificial Neural Network models can perform better that logistic regression models with data sets made of all ordinal variables, which has not been done so far. All the publications in this area were about either dichotomous or a combination of dichotomous and continuous variables.
The second objective of this study was to develop a mathematical function that can produce a measure to evaluate the direct association between exposure and possible outcome variables. This function was referred to as the function of Magnitude-of-Effect (MoE). Safety experts and practitioners can use the MoE function to interpret how strongly an exposure variable can affect the possible outcome variable. The significance of such achievement is that it can eliminate the artificial neural network models’ shortcoming and make them more applicable in the occupational safety and health engineering field.
The result of this study showed that artificial neural network models performed significantly better than logistic regression models with a data set of all ordinal variables. And also the suggested MoE function was capable and valid enough to show any correlation between exposure and possible outcome variables.