Summary
Objectives: Demonstration of the applicability of a framework called indirect classification
to the example of glaucoma classification. Indirect classification combines medical
a priori knowledge and statistical classification methods. The method is compared
to direct classification approaches with respect to the estimated misclassification
error.
Methods: Indirect classification is applied using classification trees and the diagnosis of
glaucoma. Misclassification errors are reduced by bootstrap aggregation. As direct
classification methods linear discriminant analysis, classification trees and bootstrap
aggregated classification trees are utilized in the problem of glaucoma diagnosis.
Misclassification rates are estimated via 10-fold cross-validation.
Results: Indirect classification techniques reduce the misclassification error in the context
of glaucoma classification compared to direct classification methods.
Conclusions: Embedding a priori knowledge into statistical classification techniques can improve
misclassification results. Indirect classification offers a framework to realize this
combination.
Keywords
Glaucoma - classification - decision trees
