Prediction of occupational accidents using decision tree approach

The focus of the present study is to build a predictive model which not only could predict the occupational incidents but also provide rules for explaining accident scenarios like near-miss, property damage, or injury cases. Classification and regression tree (CART) is used for prediction purpose. Furthermore, the parameters of CART have been tuned by grid based tuning and genetic algorithm (GA). The experimental results show that the GA optimized CART provides better accuracy than others. Additionally, the best rules extracted from GA optimized CART are discussed in order to adopt better safety precautionary measures at work.

[1]  Järvenpää Ae.  [Safety and health at work]. , 1972, Sairaanhoitaja. Sjukskoterskan.

[2]  Margaret E. Roberts,et al.  A Model of Text for Experimentation in the Social Sciences , 2016 .

[3]  Javier Taboada,et al.  Explaining and predicting workplace accidents using data-mining techniques , 2011, Reliab. Eng. Syst. Saf..

[4]  Li-Yen Chang,et al.  Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Moshe Ben-Akiva,et al.  Text analysis in incident duration prediction , 2013 .

[7]  Sou-Sen Leu,et al.  Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry. , 2012, Accident; analysis and prevention.

[8]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[9]  Jhareswar Maiti,et al.  Text mining based safety risk assessment and prediction of occupational accidents in a steel plant , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[10]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[11]  Frantisek Babic,et al.  Building the prediction model from the aviation incident data , 2014, 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[12]  Ercan Erdis,et al.  Decision tree analysis of construction fall accidents involving roofers , 2015, Expert Syst. Appl..

[13]  Kaija Leena Saarela,et al.  Global estimates of occupational accidents , 2006 .

[14]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[15]  M. Bevilacqua,et al.  Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach. , 2008, Accident; analysis and prevention.

[16]  In-So Kweon,et al.  Ambiguous Surface Defect Image Classification of AMOLED Displays in Smartphones , 2016, IEEE Transactions on Industrial Informatics.