Expert derived automatically generated classification trees: an example from pediatric cardiology

Classification trees provide an attractively transparent discrimination technique and may be derived either from expert opinion or from data analysis. The authors considered a real and complex problem concerning the diagnosis of babies with suspected 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 trees derived from analysis of 571 past cases both for the full problem and for a subset of 6 diseases. Automatic methods for tree creation had problems with rare diseases. Inclusion of `costs of misclassification´ feedback on the training dataset improved the performance of data derived trees though they were generally outperformed by the expert tree