DATA MINING FOR OCCUPATIONAL INJURY RISK: A CASE STUDY

Abstract

The management of occupational injury is of strategic importance in a company from the organizational, engineering and economic point of view. This work is an attempt to apply data mining techniques to data regarding accidents in a medium-sized refinery. Several techniques were adopted in order to identify the important relationships between risk level and immediate/root causes and corrective actions. As a Data Mining technique were tested: Negative Binomial Regression (NBR), Chi-Squared Automatic Interaction Detection (CHAID); Exhaustive CHAID; Classification And Regression Trees (CART); Quick, Unbiased, Efficient Statistical Tree (QUEST), Artificial Neural Network (ANN) and Neuro-Fuzzy Systems (FIS). The comparison carried out in this study shows through a real application the flexibility and advantages of using the neuro-fuzzy network, a typical soft computing tool. Using these innovative techniques to analyse injury data this study aims to: • obtain a classification of input data according to their importance and/or influence on the risk level in injuries; • assess how a variation in one or more pieces of input data can effect occupational injury and subsequently carry out a sensitivity analysis concerning the probability, the consequences and risks of the injurie events; The analyses carried out indicated important relationships between the variables, providing useful decision-making rules which can be followed when adopting measures for improvement.