Machine learning for text mining based on prediction of occupational accidents and safety risk calculation
Autour(s)
- Chang Li, Bing Pan, Zheng Xiang, Lixuan Zhang, Lee Chen, Don Chen
Abstract
Occupational accidents are a serious threat to any organization. Occupational accidents in steel industry sector remain a threat as workforce is exposed to different kinds of hazards due to the workplace characteristics. In this study, a unique method is proposed by developing a text mining based prediction model using fault tree analysis (FTA), and Bayesian Network (BN). Free unstructured accident dataset for a period of four years has been used in this study. Text mining approach results in finding the basic events concerning each of primary causes. The basic events, in turn, are utilized in building FT and BN diagram that could predict the occurrence of accidents attributable to different primary causes. The model, so developed, can be considered adequate with 87.5% accuracy. Furthermore, sensitivity analysis is performed for the validation of the model.