Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran.

Abstract

Background: A large number of occupational accidents happen at steel industries in Iran. The information about these accidents is recorded by safety offices. Data mining methods are one of the suitable ways for using these databases to create useful information. Classification and regression trees (CART) and chisquare automatic interaction detection (CHAID) are two types of a decision tree which are used in data mining for creating predictions. These predictions could show characteristics of susceptible people exposed to occupational accidents. This study was aimed to predict the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran.Design and methods: In this study, the data of 12 variables for 2127 cases of occupational injuries (including three categories of minor, severe and fatal) from 2001 to 2014 were collected. CART and CHAID algorithms in IBM SPSS Modeler version 18 were used to create decision trees and predictions.Results: Five predictions for the outcome of occupational accidents were created for each method. The most important predictor variables for CART method included age, the cause of accident and level of education respectively. For CHAID method, age, place of accident and level of education were the most important predictor variables respectively. Furthermore the accuracy of CART and CHAID methods were 81.78% and 80.73%, respectively for predictions.Conclusions: CART and CHAID methods can be used to predict the outcome of occupational accidents in the steel industry. Thus the rate of injuries can be reduced by using the predictions for employing preventive measures and training in the steel industry.Significance for public healthThe aim of this paper is to show that classification and regression trees (CART) and chi-square automatic interaction detection (CHAID) techniques can be used for detection of vulnerable individuals exposed to the occupational accidents at steel plants.