BAYESIAN SCORING SYSTEMS FOR MILITARY PELVIC AND PERINEAL BLAST INJURIES: IS IT TIME FOR A NEW APPROACH?

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 Naive 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