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- W1989760732 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." @default.
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- W1989760732 date "1996-01-30" @default.
- W1989760732 modified "2023-10-16" @default.
- W1989760732 title "AN EMPIRICAL COMPARISON OF EXPERT-DERIVED AND DATA-DERIVED CLASSIFICATION TREES" @default.
- W1989760732 doi "https://doi.org/10.1002/(sici)1097-0258(19960130)15:2<157::aid-sim149>3.0.co;2-5" @default.
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