Constructing a minimal diagnostic decision tree

D. P. McKenzie, P. D. McGorry, C. S. Wallace, L. H. Low, D. L. Copolov, B. S. Singh

Research output: Contribution to journalArticleResearchpeer-review

45 Citations (Scopus)


Classification trees and discriminant function analysis were employed in order to ascertain whether a small number of diagnostic decision rules could be extracted from a large inventory of items. Several models, involving up to 17 symptoms, that led to a broad psychiatric diagnosis were then tested on a small validation sample of 53 patients. All methods, with the exception of CART used without any pruning, generated identical trees involving four items. Almost 90% of the validation sample was able to be correctly classified by all methods although poor classification, performance was noted in the case of one particular diagnosis, Schizoaffective Psychosis. In contrast, stepwise linear discriminant analysis originally selected 17 items, although three out of the first four items selected were identical to those chosen by the tree-building methods. Although more research is required, there are indications that the latter methods may be usefully employed in constructing parsimonious decision trees.

Original languageEnglish
Pages (from-to)161-166
Number of pages6
JournalMethods of Information in Medicine
Issue number2
Publication statusPublished - 1 Jan 1993
Externally publishedYes

Cite this