Diagnosis of Glaucoma by Indirect Classifiers

A. Peters; B. Lausen; G. Michelson; O. Gefeller

doi:10.1055/s-0038-1634214

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2003; 42(01): 99-103
DOI: 10.1055/s-0038-1634214

Original article

Schattauer GmbH

Diagnosis of Glaucoma by Indirect Classifiers

Authors

A. Peters

¹Department of Medical Informatics, Biometry and Epidemiology, Germany
B. Lausen

¹Department of Medical Informatics, Biometry and Epidemiology, Germany
G. Michelson

²Department of Ophthalmology and Eye Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
O. Gefeller

¹Department of Medical Informatics, Biometry and Epidemiology, Germany

Abstract

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

Full Text

References

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