Premium
Direct and Indirect Classification in Clinical Research
Author(s) -
Peters A.,
Lausen B.
Publication year - 2003
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200390059
Subject(s) - a priori and a posteriori , computer science , machine learning , artificial intelligence , monte carlo method , pattern recognition (psychology) , data mining , statistics , mathematics , philosophy , epistemology
We investigate if the use of a priori knowledge allows an improvement of medical decision making. We compare two frameworks of classification – direct and indirect classification – with respect to different classification errors: differential misclassification, observed misclassification and true misclassification. We analyze general behaviors of the classifiers in an artificial example and furthermore as being interested in the diagnosis of early glaucoma we adapt a simulation model of the optic nerve head. Indirect classifiers outperform direct classifiers in certain parameter situations of a Monte‐Carlo study. In summary, we demonstrate that indirect classification provides a flexible framework to improve diagnostic rules by using explicit a priori knowledge in clinical research.