Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Machine Learning Approach to Classifying Fatal and Non-Fatal Accidents in Industries

사망사고와 부상사고의 산업재해분류를 위한 기계학습 접근법

  • Kang, Sungsik (Department of Safety Engineering, Pukyong National University) ;
  • Chang, Seong Rok (Department of Safety Engineering, Pukyong National University) ;
  • Suh, Yongyoon (Department of Industrial and Systems Engineering, Dongguk University(Seoul Campus))
  • 강성식 (부경대학교 안전공학과) ;
  • 장성록 (부경대학교 안전공학과) ;
  • 서용윤 (동국대학교(서울캠퍼스) 산업시스템공학과)
  • Received : 2021.06.24
  • Accepted : 2021.08.05
  • Published : 2021.10.31
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Abstract

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

Keywords

Acknowledgement

이 성과는 2020년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받 아 수행된 연구임(No. NRF-2020R1C1C1007302).

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