NDLI: A comparison of missing value imputation methods for classifying patient outcome following trauma injury

Content Provider	IEEE Xplore Digital Library
Author	Penny, K.I. Chesney, T.
Copyright Year	2008
Description	Author affiliation: Sch. of Accounting Econ. & Stat., Napier Univ., Edinburgh (Penny, K.I.)
Abstract	A study is designed to compare several missing value imputation methods to enable classification of patient outcome following trauma injury. The Glasgow coma score is a measure of head injury severity, and is known to be important in determining patient outcome. The Glasgow coma scores are missing for 12% of the dataset, and in order to classify patient outcome for these patients, the missing values are first imputed. The first part of the study is designed to compare the performance of several missing value imputation methods, and errors between imputed values and known values of Glasgow coma scores are calculated. The second part of the study involves analysing the imputed data sets using logistic regression to classify whether patients live or die. Accuracy of results are compared in terms of sensitivity, specificity, positive predictive value and negative predictive value.
Starting Page	367
Ending Page	370
File Size	115172
Page Count	4
File Format	PDF
ISBN	9789537138127
ISSN	13301012
DOI	10.1109/ITI.2008.4588437
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	SRCE University, University of Zagreb, Croatia
Subject Keyword	Logistics Information technology Hospitals Trauma injury Missing value imputation Logistic regression
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

Imputing missing values: the effect on the accuracy of classification.

Comparing ANNs to logistic regression for predicting trauma survival

Comparison of imputation techniques after classifying the dataset using knn classifier for the imputation of missing data.

Multiple Imputation for Missing Data Using Genetic Programming

A Comparison of Five Methods for Missing Value Imputation in Data Sets

RESEARCH Open Access Shrinkage regression-based methods for microarray missing value imputation

A Comparison of Five Methods for Missing Value Imputation in Data Sets

A Comparison of Five Methods for Missing Value Imputation in Data Sets

A comparison of missing value imputation methods for classifying patient outcome following trauma injury

Similar Documents

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

Imputing missing values: the effect on the accuracy of classification.

Comparing ANNs to logistic regression for predicting trauma survival

Comparison of imputation techniques after classifying the dataset using knn classifier for the imputation of missing data.

Multiple Imputation for Missing Data Using Genetic Programming

A Comparison of Five Methods for Missing Value Imputation in Data Sets

RESEARCH Open Access Shrinkage regression-based methods for microarray missing value imputation

A Comparison of Five Methods for Missing Value Imputation in Data Sets

A Comparison of Five Methods for Missing Value Imputation in Data Sets

A comparison of missing value imputation methods for classifying patient outcome following trauma injury