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

  • Gholam Abbas Shirali Department of Occupational Health Engineering, Faculty of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, Islamic Republic of.
  • Moloud Valipour Noroozi | mouloodvalipour@yahoo.com Department of Occupational Health Engineering, Faculty of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, Islamic Republic of.
  • Amal Saki Malehi Department of Biostatistics and Epidemiology, Faculty of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, Islamic Republic of.

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.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.
Published
2018-11-08
Info
Issue
Section
Original Articles
Supporting Agencies
Ahvaz Jundishapour University of Medical Sciences
Keywords:
Decision trees, Occupational injuries, Steel
Statistics
  • Abstract views: 866

  • PDF: 458
  • HTML: 36
How to Cite
Shirali, G. A., Valipour Noroozi, M., & Saki Malehi, A. (2018). Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran. Journal of Public Health Research, 7(2). https://doi.org/10.4081/jphr.2018.1361