Explaining and predicting workplace accidents using data-mining techniques

T. Rivas, M. Paz, Martín, J.E., Matías, J.M., García, J.F. and J. Taboada

Reliability Engineering and System Safety, 2011, vol. 96, issue 7, 739-747

Abstract: Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

Keywords: Workplace accidents; Classification trees; Data mining; Bayesian networks; Support vector machines; Mine and construction safety (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:96:y:2011:i:7:p:739-747

DOI: 10.1016/j.ress.2011.03.006

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