An Application of Fuzzy Prototypes to the Diagnosis and Treatment of Fuzzy Diseases

Abstract

Decision support systems, embedded in modern telemedicine applications, are a tool to improve the skills of general practitioners and patients in decision-making in medicine. Nowadays, one of the more challenging problems in this context is how to diagnose those diseases, whose early clinical signs are often subtle, and many of their common signs and symptoms are similar to other. These “fuzzy diseases,” even they can have distinctive features, are not diagnosable through a concrete clinical test or symptom, and, thus, they are difficult to recognize, especially in their initial phases when they might be mistaken for other similar ones. Then, the diagnosis of a fuzzy disease set is based on the exclusion of symptoms and tests results, due to the similarity between them. In the present article, it is proposed the development of a Clinical Decision Support System framework to diagnose a set of fuzzy diseases, concretely applied to Fibromyalgia and associated syndromes. For this purpose, in this paper a reasoning method that uses theories about conceptual categorization from the psychology, pattern recognition, and Zadeh's prototypes has been designed. Through the use of this model, satisfactory results in the evaluation of patients were obtained.