A machine learning methodology for the analysis of workplace accidents

Abstract

This article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). The approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. The results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes.

Keywords:

• Machine learning
• Statistics
• Operations research
• Data mining
• Artificial intelligence

Acknowledgements

We wish to thank Decision Systems Laboratory of the University of Pittsburg (http:// dsl.sis.pitt.edu) for generously ceding the GeNIe system with which some of the models in this research were built. J. M. Matíasapos; research is supported by the Spanish Ministry of Education and Science, grant No. MTM2005-00820.