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Abstract #8672 Published in IGR 5-1
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy
Hothorn T; Lausen BArtificial Intelligence in Medicine 2003; 27: 65-79
Diagnosis based on medical image data is common in medical decision making and clinical routine. The authors discuss a strategy to derive a classifier with good performance on clinical image data and to justify the properties of the classifier by an adapted simulation model of image data. They focus on the problem of classifying eyes as normal or glaucomatous based on 62 routine explanatory variables derived from laser scanning images of the optic nerve head. As learning sample, they use a case-control study of 98 normal and 98 glaucomatous subjects matched by age and sex. Aggregating multiple unstable classifiers allows substantial reduction of misclassification error in many applications and bench mark problems. The authors investigate the performance of various classifiers for the clinical learning sample as well as for a simulation model of eye morphologies. Bagged classification trees (bagged-CTREE) are compared to single classification trees and linear discriminant analysis (LDA). They additionally compare three estimators of misclassification error: ten-fold cross-validation, the 0.632+ bootstrap and the out-of-bag estimate. In summary, the application of the authors' strategy of a knowledge-based decision support shows that bagged classification trees perform best for glaucoma classification.
Dr. T. Hothorn, Institut fur Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Waldstrasse 6, D-91054, Erlangen, Germany
Classification:
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
6.9.1 Laser scanning (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
15 Miscellaneous
