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

Diagnosis based on medical image data is common in clinical research 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. As learning set 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 are compared to single classification trees and linear discriminant analysis (LDA). We additionally compare three estimators of misclassification error: 10-fold cross-validation, the .632+ bootstrap and the out-of-bag estimate. In summary, the application of our strategy shows that bagged classification trees perform best in our example