Blood loss severity prediction using game theoretic based feature selection

Detection of hypovolemia in the early stages of hemorrhage is an important but unsolved problem in medicine. Many preventable deaths amongst critically injured patients happen due to delayed treatment of uncontrolled hemorrhage. Using a database of physiological signals collected during simulated hemorrhage on human subjects, our research applies a variety of signal processing techniques to extract a multitude of features that enable the prediction of the severity of hemorrhage. In this study, a novel feature selection method based on coalition game theory has been proposed which helps identify the most valuable features and thereby reduce the size of the feature space. This reduction in feature space not only improves the efficiency, but also improves the prediction accuracy and reliability of the developed model. This feature selection algorithm is independent of the underlying classification method and can be combined with any classification method based on the employed data. The proposed feature selection method significantly enhances the prediction accuracy by optimally selecting the features compared to the state of the art.