Abstract
Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques. An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian (NB) model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance. Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS and the Revised Trauma Score in virtually all areas; Positive Predictive Value 0.8941, Specificity 0.9027, Accuracy 0.9056 and Area Under Curve 0.9059. A two-sample t-test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p<0.001). With limited resources and the simplest of Bayesian methodologies we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for the military, but also in civilian trauma.
