Automated Disease Classification in Cytometry
The development of disease classifiers is of high interest for the standardized and automated information extraction from multiparameter flow cytometric list mode and other (e.g. clinical chemistry) data.
Various statistical, cluster analysis, neural network, expert system, triple matrix, principal component, expert system and fuzzy logic classifiers have been proposed for cytometric data classification.
It is the intention of this forum to shortly discuss the concepts of various classification methods, to comparatively classify list mode data sets of diseased patients and normal individuals and to display the respective results.