An empirical comparison of expert-derived and data-derived classification trees

Statistics in Medicine
M ChiognaK Bull

Abstract

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.

Citations

Jun 15, 2007·Diabetes Care·Kathryn M ThrailkillJohn L Fowlkes
Mar 29, 2001·Journal of Surgical Oncology·P M SimpsonJ S Spratt
May 30, 2001·Critical Care Medicine·J M TilfordD H Fiser

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