The complementary process of fuzzy medical diagnosis and its properties

doi:10.1016/0020-0255(86)90023-X

Information Sciences

Volume 38, Issue 3, June 1986, Pages 229-242

https://doi.org/10.1016/0020-0255(86)90023-X Get rights and content

Abstract

The composite fuzzy relational equation and its applications to the medical diagnosis were first proposed by Sanchez. He also gave the method to derive the medical knowledge from the clinical data, which consist of two fuzzy relations on a set of patients and a set of propositions that represent symptoms or diagnoses. His method, however, gives attention to only one view of these data. In this paper the complementary process to Sanchez's fuzzy diagnosis process is introduced by reinterpreting the clinical data on the domains of negated propositions. Some properties of the fuzzy diagnosis processes are also given.

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E. Sanchez
Solutions in composite fuzzy relation equations: Application to medical diagnosis in Brouwerian logic

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Citation Excerpt :
According to [25], when the solution set of fuzzy relation equations (1) is non-empty, then it is, in general, a non-convex set which can be completely determined by one maximum solution and a finite number of minimal solutions. The resolution of fuzzy relation equations is one of the most important problems in the field of fuzzy sets and fuzzy systems [1–11,14,17–22,25–29,31–35,38,40–66,70,73,76] and also, it is applied in optimization problems (see, for instances, Refs. [12,36,37,39,72]). In [30] it has been established that the majority of fuzzy inference systems can be implemented by using the fuzzy relation equations.
This paper studies the problem of solving a max-T composite finite fuzzy relation equation, where T is a special class of pseudo-t-norms. If the equation is solvable, then its set of feasible solutions is determined by the greatest solution and a finite number of minimal solutions. Some necessary conditions are presented for the minimal solutions in terms of the maximum solution and zero value. Under these conditions, some minimal solutions of the system can be obtained easily. Some procedures are also proposed in order to simplify the original system. The simplified system is then decomposed (if possible) into several subsystems with smaller dimensions, which are very easy to solve. Furthermore, a method is presented to solve each subsystem. By combining the method and those procedures, an efficient algorithm is proposed to obtain the set of feasible solutions of the original system. Two examples are also given to illustrate the algorithm.
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One promising way to increase the classification accuracy of medical decision support systems is to implement heuristic combinations of pattern recognition and artificial intelligence tools. A parallel between “cognition” model and differential diagnostic task is sketched accentuating the aggregation of activating and restraining inputs and corresponding PRO and CON evidence in medicine. On the basis of this paradigm a trainable model of a fuzzy neuron is proposed which resembles some elements from the physician's decision process. An example from aviation medicine is presented which demonstrates the enhanced performance.
Author's reply
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Pattern recognition with fuzzy sets is considered with regard to medical diagnostic problems. An alternative interpretation of membership grade is suggested which expresses the degree of ‘illness’ of a patient with respect to a certain disease. A formal statement of a two-level fuzzy classifier is presented. The main emphasis is put on additional criteria designed to evaluate a fuzzy diagnostic decision for an unknown object. Two groups of criteria are formed-decision-making connected, and domain-dependent. The decision-making group of criteria can be viewed as related to the ‘soft’ consensus measure in group decisions theory. Interconnections between criteria are discussed. The notions proposed are illustrated with real medical data from the fields of aviation medicine and neonatology.

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The complementary process of fuzzy medical diagnosis and its properties

Abstract

Inform. and Control

Inform. and Control

Inform. and Control

Solutions in composite fuzzy relation equations: Application to medical diagnosis in Brouwerian logic