A new survival status prediction system for severe trauma patients based on a multiple classifier system. (April 2017) - Sanz, José; Paternain, Daniel; Galar, Mikel; Fernandez, Javier; Reyero, Diego; Belzunegui, Tomás
Highlights: The survival status of trauma patients is tackled as a classification problem. A new prediction system based on a multiple classifier system is proposed. The study is carried out over 462 patients that were treated at the Hospital of Navarre in Spain. The new approach is developed using the specific features of the patients stored in the Major Trauma Registry of Navarre. The results show that the usage of the new proposal enhance the performance of the currently used systems. Abstract: Background and Objective: Severe trauma patients are those who have several injuries implying a death risk. Prediction systems consider the severity of these injuries to predict whether the patients are likely to survive or not. These systems allow one to objectively compare the quality of the emergency services of trauma centres across different hospitals. However, even the most accurate existing prediction systems are based on the usage of a single model. The aim of this paper is to combine several models to make the prediction, since this methodology usually improves the performance of single models. Materials and Methods: The two currently used prediction systems by the Hospital of Navarre, which are based on logistic regression models, besides the C4.5 decision tree are combined to conform our proposed multiple classifier system. The quality of the method is tested using the major trauma registry of Navarre, which stores information of 462 trauma patients. A 10x10-fold cross-validation model is applied using as performance measures the specificity, sensitivity and the geometric mean between the two former ones. The results are supported by the usage of the Mann–Whitney's U statistical test. Results: The proposed method provides 0.8908, 0.6703 and 0.7661 for sensitivity, specificity and geometric mean, respectively. It slightly decreases the sensitivity of the currently used systems but it notably increases the specificity, which implies a large enhancement on the geometric mean. The same behaviour is found when it is compared versus four classical ensemble approaches and the random forest. The statistical analysis supports the quality of our proposal, since the obtained p-values are less than 0.01 in all the cases. Conclusions: The obtained results show that the multiple classifier systems is the best choice among the considered methods to obtain a trade-off between sensitivity and specificity.