Imputation methods to deal with missing values when data mining trauma injury data

Methods for analysing trauma injury data with missing values, collected at a UK hospital, are reported. One measure of injury severity, the Glasgow coma score, which is known to be associated with patient death, is missing for 12% of patients in the dataset. In order to include these 12% of patients in the analysis, three different data imputation techniques are used to estimate the missing values. The imputed data sets are analysed by an artificial neural network and logistic regression, and their results compared in terms of sensitivity, specificity, positive predictive value and negative predictive value.