FUZZY INFERENCE AND MEDICAL DIAGNOSIS, A CASE STUDY

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In the framework of inflammatory protein variations, it is illustrated a methodology based on fuzzy logic for diagnosis assistance. The pattern of medical knowledge involves five proteins and eleven groups: eight inflammatory syndromes, two non inflammatory syndromes and the normal condition. Most of the inflammatory syndromes are not typical for protein variations can be dissociated, so that a global analysis of the problem has to be considered. Choosing as an example, Vasculitis (three protein levels are increased, while two others are decreased or normal), it is shown how the biomedical knowledge can be expressed linguistically to be then translated into possibility distributions. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. In some cases, influence of proteins in terms of importance operations is not symmetrical. To face such situations, a calibration of soft-AND operators is performed. These aggregation procedures are illustrated with an example. Defuzzification of results (i.e. diagnostic groups assigned to patients) is presented as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control.
バイオメディカル・ファジィ・システム学会の論文

FUZZY INFERENCE AND MEDICAL DIAGNOSIS, A CASE STUDY

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