Fuzzy diagnosis.
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Diagnosis, Computer-Assisted↗
Biomedical subjects
L I Kuncheva
Publications and source records attributed to L I Kuncheva.
Fuzzy multi-level classifier for medical applications.
In this paper a fuzzy pattern recognition model is described, which is a tool to handle problems with noncrisp and multi-class membership of the objects. It is oriented to medical diagnostics, where the patients suffer from more than one disease in different degrees. Fuzzy pattern recognition is supposed to fit medical diagnostic problems better than conventional pattern recognition. The design of a multi-level fuzzy decision scheme is considered in order to derive high performance, taking into account expert logic and human experience. Two main topics are discussed--the criterion for evaluation of classification accuracy and the training rule. The implementation of fuzzy multi-level classifier is illustrated with real clinical data.
Aerospace Medicine↗
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.
Atmospheric Pressure↗