Evaluation of partial classification algorithms using ROC curves.
When using computer programs for decision support in clinical routine, an assessment or a comparison of the underlying classification algorithms is essential. In classical (forced) classification, the classification rule always selects exactly one alternative. A number of proven discriminant measures are available here, e.g.sensitivity and error rate. For probabilistic classification, a series of additional measures has been developed [1]. However, for many clinical applications, there are models where an observation is classified into several classes (partial classification), e.g., models from artificial intelligence, decision analysis, or fuzzy set theory. In partial classification, the discriminatory ability (Murphy) can be adjusted a priori to any level, in most practical cases. Here the usual measures do not apply. We investigate the preconditions for assessment and comparison based on medical decision theory. We focus on problems in the medical domain and establish a methodological framework. When using partial classification procedures, a ROC analysis in the classical sense is no longer appropriate. In forced classification for two classes, the problem is to find a cutoff point on the ROC curve; while in partial classification, you have to find two of them. They characterize the elements being classified as coming from both classes. This extends to several classes. We propose measures corresponding to the usual discriminant measures for forced classification (e.g., sensitivity and error rate) and demonstrate the effects using the ROC approach. For this purpose, we extend the existing method for forced classification in a mathematically sound manner. Algorithms for the construction of thresholds can easily be adapted. Two specific measurement models, based on parametric and non-parametric approaches, will be introduced. The basic methodology is suitable for all partial classification problems, whereas the extended ROC analysis assumes a rank order of the selected alternatives. The method is based on the black box principle and makes use only of the results of the compared algorithms and some general intuitive principles. Therefore, algorithms arising from different "philosophical" approaches may also be compared according to their results. For demonstration purposes, a clinical example from cranial computed tomography is presented. Linear discriminant analysis as a reference is compared to a Bayesian and a fuzzy procedure.
Tusch
G
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Clinic for Abdominal and Transplantation Surgery, Medical School Hannover, 30623 Hannover, Germany.
D000465:Algorithms; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000145:classification / Q000000981:diagnostic imaging; D002965:Classification / Q000379:methods*; D016001:Confidence Intervals; D003936:Diagnosis, Computer-Assisted; D017143:Fuzzy Logic; D006801:Humans; D016014:Linear Models; D012372:ROC Curve; D012886:Skull / Q000000981:diagnostic imaging; D012888:Skull Neoplasms / Q000145:classification / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed