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Classification to ordinal categories using a search partition methodology with an application in diabetes screening
Author(s) -
Marshall Roger J.
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19991030)18:20<2723::aid-sim234>3.0.co;2-1
Subject(s) - ordinal data , ordinal regression , partition (number theory) , mathematics , ordinal scale , ordered logit , ordinal optimization , binary number , statistics , logistic regression , computer science , artificial intelligence , combinatorics , arithmetic
A method is proposed for classification to ordinal categories by applying the search partition analysis (SPAN) approach. It is suggested that SPAN be repeatedly applied to binary outcomes formed by collapsing adjacent categories of the ordinal scale. By a simple device, whereby successive binary partitions are constrained to be nested, a partition for classification to the ordinal states is obtained. The approach is applied to ordinal categories of glucose tolerance to discriminate between diabetes, impaired glucose tolerance and normal states. The results are compared with analysis by ordinal logistic regression and by classification trees. Copyright © 1999 John Wiley & Sons, Ltd.