AN EMPIRICAL COMPARISON OF EXPERT-DERIVED AND DATA-DERIVED CLASSIFICATION TREES

✍ Scribed by M. CHIOGNA; D. J. SPIEGELHALTER; R. C. G. FRANKLIN; K. BULL

• Book ID: 102650245
• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 786 KB
• Volume: 15
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19960130)15:2<157::aid-sim149>3.0.co;2-5

✦ Synopsis

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.