An application of the evidential reasoning rule to predict outcomes following traumatic injuries

The evidential reasoning (ER) rule for multi-attribute classification has been recently developed, and has enhanced the properties of the Dempster's rule through defining the weight and reliability of the evidence. This paper aims to introduce the ER rule for data classification and ensemble learning in the domain of trauma research. Moreover, it aims to identify multiple methods for building trauma prediction models, and to increase model accuracy in order to enhance the care services provided to trauma patients. The model proposed in this paper includes age, gender, age and gender combined, injury severity score, Glasgow coma scale, and modified Charlson comorbidity index as predictors of patient outcomes at 30 days or at discharge, whichever occurred first. The results of some machine learning (ML) algorithms such as decision tree, random forest, and artificial neural networks are compared to logistic regression results. The area under the curve (AUC) result of the artificial neural networks algorithm is 0.9076, which outperforms that of the logistic regression presented in the paper, i.e. 0.9045. Moreover, the application of the ER rule for ensemble learning indicates adequate prediction performance.