Abstract
Medical decision making is based on various diagnostic measurements and the evaluation of anamnestic information. For example glaucoma diagnosis is based on several direct and indirect assessments of the eye. We discuss possibilities to use a definition of glaucoma to improve supervised glaucoma classification by laser scanning image data. The learning sample consists of laser scanning image data and other diagnostic measurements.
We discuss indirect supervised classification proposed by Hand et al. (2001), which provides a framework to incorporate the full information of the learning sample as intermediate variables. We compare direct classifiers like linear discriminant analysis, classification trees and bagged classification trees with indirect classification. Comparing bagged direct and bagged indirect classifiers, we achieve a reduction of estimated misclassification error from 18.3% to 15.4%.
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Peters, A., Hothorn, T., Lausen, B. (2002). Glaucoma Diagnosis by Indirect Classifiers. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_51
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DOI: https://doi.org/10.1007/978-3-642-56181-8_51
Publisher Name: Springer, Berlin, Heidelberg
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