Global decisions taking on the basis of dispersed medical data

Abstract

The main aim of the article is to present a decision-making system using dispersed knowledge. The article introduces the system with dynamically generated coalitions. The local knowledge bases, on the basis of which a similar classification for the test object is made, are combined into a coalition. In the proposed system, the classification process can be divided into several steps. In the first step we describe the classification of a test object made on the basis of local knowledge base, by probability vectors over decision classes. We cluster local knowledge bases with respect to similarities of probability vectors. For every cluster, we find a kind of combined information. Finally, we classify the test object using the method for the conflict analysis. The main aim of the paper is to present the results of experiments on medical data. In experiments the situation is considered in which medical data from one domain are collected in many medical centers. We want to use all of the collected data at the same time in order to make a global decisions. © 2013 Springer-Verlag.