Constructing a minimal diagnostic decision tree.
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.
McKenzie
D P
DP
Department of Psychological Medicine, Monash University, Melbourne, Australia.
McGorry
P D
PD
Wallace
C S
CS
Low
L H
LH
Copolov
D L
DL
Singh
B S
BS
D000465:Algorithms; D001185:Artificial Intelligence; D001714:Bipolar Disorder / Q000175:diagnosis; D003663:Decision Trees; D003866:Depressive Disorder / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D011569:Psychiatric Status Rating Scales; D011618:Psychotic Disorders / Q000175:diagnosis; D012559:Schizophrenia / Q000175:diagnosis; D012984:Software