Machine Learning Approach to Classifying Fatal and Non-Fatal Accidents in Industries 


Vol. 36,  No. 5, pp. 52-60, Oct.  2021
10.14346/JKOSOS.2021.36.5.52


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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.

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  Cite this article

[IEEE Style]

강성식, 장성록, 서용윤, "Machine Learning Approach to Classifying Fatal and Non-Fatal Accidents in Industries," Journal of the Korean Society of Safety, vol. 36, no. 5, pp. 52-60, 2021. DOI: 10.14346/JKOSOS.2021.36.5.52.

[ACM Style]

강성식, 장성록, and 서용윤. 2021. Machine Learning Approach to Classifying Fatal and Non-Fatal Accidents in Industries. Journal of the Korean Society of Safety, 36, 5, (2021), 52-60. DOI: 10.14346/JKOSOS.2021.36.5.52.

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