  ICU mortality scoring systems attempt to predict patient mortality using
predictive models with various clinical predictors. Examples of such systems
are APACHE, SAPS and MPM. However, most such scoring systems do not actively
look for and include interaction terms, despite physicians intuitively taking
such interactions into account when making a diagnosis. One barrier to
including such terms in predictive models is the difficulty of using most
variable selection methods in high-dimensional datasets. A genetic algorithm
framework for variable selection with logistic regression models is used to
search for two-way interaction terms in a clinical dataset of adult ICU
patients, with separate models being built for each category of diagnosis upon
admittance to the ICU. The models had good discrimination across all
categories, with a weighted average AUC of 0.84 (>0.90 for several categories)
and the genetic algorithm was able to find several significant interaction
terms, which may be able to provide greater insight into mortality prediction
for health practitioners. The GA selected models had improved performance
against stepwise selection and random forest models, and provides greater
flexibility in terms of variable selection by being able to optimize over any
modeler-defined model performance metric instead of a specific variable
importance metric.

Neural and Evolutionary Computing;Machine Learning;Machine Learning