Evaluation of computerized medical diagnostic decisions via fuzzy sets

Two of the greatest obstacles in medical data processing can be summarized as 'multiclass and noncrisp membership of the patterns' and originate from the fact, that one patient can suffer in different degrees from more than one disease simultaneously. Some encouraging results have been obtained applying to these problems a fuzzy pattern recognition model instead of the purely statistical one. A criterion for evaluation of fuzzy classification accuracy has been developed. In this paper, its general form is presented and appropriately discussed. A composed form of the criterion is suggested, which consists of three components - Integral Index of Classification. Highest Discrepancy Index of Classification and Highest Coincidence Index of Classification. Three graphical examples have been designed in order to clear up the underlying idea. The implementation of the criterion is illustrated by experiments with real data from medical practice.