Developing the Function of 'Magnitude-of-Effect' (MoE) for Artificial Neural Networks to Demonstrate the Causal Effect of Exposure Variables on Outcome Variable

作者：Moayed Farman A^*; Shell Richard L

来源：Annals of Occupational Hygiene, 2011, 55(2): 143-151.

DOI：10.1093/annhyg/meq080

• artificial neural network
• logistic regression
• Magnitude-of-Effect
• ordinal variables
• variable correlation

Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used by safety professionals and practitioners to assess the association between exposures and possible occupational disorders or diseases and predict the outcome. Recently, artificial neural network (ANN) models are gradually finding their way into safety field. It has been shown that they are capable of predicting outcomes more accurately than LR, but they are incapable of demonstrating the direct correlation between exposure variables and a possible outcome variable. The objective of this study was to develop a mathematical function that can use the result of ANN models to produce a measure for evaluating 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 outcome variable, similar to an odds ratio, which can be calculated by using estimated parameters in LR models. The significance of such achievement is that it can eliminate one of the ANN model's shortcoming and make them more applicable in the occupational safety and health engineering field.

• 出版日期2011-3