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Bagging Tree Classifiers for Laser Scanning Images: Data- and Simulation-based Strategy

Hothorn, T and Lausen, B (2003) 'Bagging Tree Classifiers for Laser Scanning Images: Data- and Simulation-based Strategy.' Artificial Intelligence in Medicine, 27 (1). pp. 65-79. ISSN 0933-3657

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Diagnosis based on medical image data is common in medical decision making and clinical routine. We 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. We 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 we 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. We 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). We additionally compare three estimators of misclassification error: 10-fold cross-validation, the 0.632+ bootstrap and the out-of-bag estimate. In summary, the application of our strategy of a knowledge-based decision support shows that bagged classification trees perform best for glaucoma classification.

Item Type: Article
Uncontrolled Keywords: Discriminant analysis; Laser scanning; Images; Bagging; Error rate; Estimation; Simulation
Subjects: H Social Sciences > HA Statistics
R Medicine > RE Ophthalmology
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 04 Jul 2012 21:48
Last Modified: 06 Jan 2022 13:24

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