Informatics and Applications
December 2013, Volume 7, Issue 4, pp 105-111
PREDICTION AND CLASSIFICATION METHOD FOR CENSORED DATA
- T. V. Zakharova
- E.M. Abramova
Abstract
The classification method for noninsulin-dependent diabetes mellitus patients cohort is presented and
the technique for identification of diabetes mellitus indicators is described. The basic medical data we dealt with
turned out to be unfit for classification. The main obstacle for applying classical discrimination approaches was
insufficiency and incompleteness of original data. For data processing, the authors suggest to select different sets of
discriminant characteristics and to obtain classification functions for each set. The number of these sets depends
on data incompleteness degree. The more data are omitted, the more different sets are needed. Each patient finally
refers to the group, for which he gets the greater number of matches in classification. This multistep procedure
reimburses small sample size and insufficiency and incompleteness of original data.
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[+] About this article
Title
PREDICTION AND CLASSIFICATION METHOD FOR CENSORED DATA
Journal
Informatics and Applications
December 2013, Volume 7, Issue 4, pp 105-111
Cover Date
2013-12-31
DOI
10.14357/19922264130411
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
hypothesis; censored data; discriminant variables; classification functions; forecasting
Authors
T. V. Zakharova  and E.M. Abramova
Author Affiliations
Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics,
M. V. Lomonosov Moscow State University, Moscow, Russia
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