Use of artificial intelligence methods for classifying diabetic patients with polyneuropathy
نویسندگان
چکیده
Materials and methods Retrospective analysis of 195 patients. The fuzzy model determined a DPN degree score (0-10) by the combination of fuzzy sets derived from clinical variables (sensorial modalities and a set of DPN-related symptoms), using if-then rules to combine the inputs with the output sets (Mamdani process), with membership functions determined by a team of 4 DPN specialists. The MCA method grouped 16 DPNrelated categorical variables [sensorial modalities, symptoms, foot inspection characteristics] into micro and macro-classes (groups) after the algorithm learned the grouping pattern of the variables in the patients’ cohort. A Kohonen map was used to better represent the clusters of variables that could identify different DPN severities.
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