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False alarm is one of the main concerns in intensive care units and canresult in care disruption, sleep deprivation, and insensitivity of care-giversto alarms. Several methods have been proposed to suppress the false alarm ratethrough improving the quality of physiological signals by filtering, anddeveloping more accurate sensors. However, significant intrinsic correlationamong the extracted features limits the performance of most currently availabledata mining techniques, as they often discard the predictors with lowindividual impact that may potentially have strong discriminatory power whengrouped with others. We propose a model based on coalition game theory thatconsiders the inter-features dependencies in determining the salient predictorsin respect to false alarm, which results in improved classification accuracy.The superior performance of this method compared to current methods is shown insimulation results using PhysionNet's MIMIC II database.

A Shapley Value Solution to Game Theoretic-based Feature Reduction in False Alarm Detection