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.