Chiogna M, Spiegelhalter D J, Franklin R C, Bull K
Department of Statistics, University of Glasgow, U.K.
Stat Med. 1996 Jan 30;15(2):157-69. doi: 10.1002/(SICI)1097-0258(19960130)15:2<157::AID-SIM149>3.0.CO;2-5.
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.
分类树提供了一种极具吸引力的透明判别技术，它既可以从专家意见中得出，也可以从数据分析中得出。我们考虑一个关于将疑似患有严重先天性心脏病的婴儿诊断为27种类型之一的真实且复杂的问题。通过临床评估获得了所有可能错误分类的完整损失矩阵。将基于专家意见得出的树与基于对571个过去病例的分析得出的树进行了比较，比较针对的是整个问题以及6种疾病的一个子集。发现自动创建和修剪树的方法对于罕见疾病存在问题，因此进行了人工修剪。纳入成本后临床性能有了很大提高，即使对于最初为最小化分类错误而构建的树也是如此。专家树展示了一种无法自动重现的特定构建策略。专家树总体上优于从数据得出的树，尤其是在识别重要复合特征的能力方面。
