Abstract:
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.
Key-Words
Classification Trees - Computer-assisted Diagnosis - Discriminant Analysis
