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

arXiv: https://arxiv.org/abs/1512.01680 · HTML: https://mlsanity.com/paper/1512.01680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2015.

## Built on by (most cited first, 1 of 1)

- [Classification of Methods to Reduce Clinical Alarm Signals for Remote Patient Monitoring: A Critical Review](https://mlsanity.com/paper/2302.03885/) (2023)

## Abstract (arXiv)

False alarm is one of the main concerns in intensive care units and can result in care disruption, sleep deprivation, and insensitivity of care-givers to alarms. Several methods have been proposed to suppress the false alarm rate through improving the quality of physiological signals by filtering, and developing more accurate sensors. However, significant intrinsic correlation among the extracted features limits the performance of most currently available data mining techniques, as they often discard the predictors with low individual impact that may potentially have strong discriminatory power when grouped with others. We propose a model based on coalition game theory that considers the inter-features dependencies in determining the salient predictors in respect to false alarm, which results in improved classification accuracy. The superior performance of this method compared to current methods is shown in simulation results using PhysionNet's MIMIC II database.