An empirical comparison of expert-derived and data-derived classification trees.
Classification trees provide an attractively transparent discrimination technique, and may be derived from both expert opinion and from data analysis. We consider a real and complex problem concerning the diagnosis of babies with suspected critical congenital heart disease into one of 27 classes. A full loss matrix for all possible misclassifications was obtained from clinical assessments. A tree derived from expert opinion was compared with those derived from analysis of 571 past cases, both for the full problem and for a subset of 6 diseases. Automatic methods for tree creation and pruning were found to have problems for rare diseases, and hand-pruning was carried out. Inclusion of costs led to much improved clinical performance, even for trees that had originally been constructed to minimize classification errors. The expert tree showed a specific building strategy that could not be reproduced automatically. The expert tree generally outperformed those derived from data, particularly in the ability to identify important composite features.
Chiogna
M
M
Department of Statistics, University of Glasgow, U.K.
Spiegelhalter
D J
DJ
Franklin
R C
RC
Bull
K
K
D000465:Algorithms; D015982:Bias; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000592:standards*; D016002:Discriminant Analysis; D005103:Expert Systems; D006330:Heart Defects, Congenital / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D007231:Infant, Newborn; D011446:Prospective Studies; D012017:Referral and Consultation; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity