Imputation Methods to Deal with Missing Values when Data Mining Trauma Injury Data

Kay Penny*, Thomas Chesney

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

14 Citations (Scopus)

Abstract

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
Original languageEnglish
Title of host publication28th International Conference on Information Technology Interfaces, 2006.
PublisherIEEE Explore
Pages213-218
DOIs
Publication statusPublished - Jun 2006
EventInformation Technology Interfaces: 2006 - Cavtat, Croatia
Duration: 19 Jun 200622 Jun 2006
Conference number: 28

Conference

ConferenceInformation Technology Interfaces
Abbreviated titleITI
Country/TerritoryCroatia
CityCavtat
Period19/06/0622/06/06

Fingerprint

Dive into the research topics of 'Imputation Methods to Deal with Missing Values when Data Mining Trauma Injury Data'. Together they form a unique fingerprint.

Cite this