Abstract: Disclosed is a clinical diagnosis supporting method and system based on hierarchical fuzzy inference. The clinical diagnosis supporting method includes the steps of: (a) extracting check data of a plurality of check items for each of a plurality of patients from a respective check item database; (b) selecting a characteristic from the check data of each of the check items; (c) hierarchical fuzzy-inferring the check data from which the characteristic is selected, for each check item; (d) extracting a check opinion for each disease based on an output value of the fuzzy inference for each check item; and (e) making a synthetic clinical estimation based on the extracted check opinion.

Abstract: Disclosed is a clinical diagnosis supporting method and system based on hierarchical fuzzy inference. The clinical diagnosis supporting method includes the steps of: (a) extracting check data of a plurality of check items for each of a plurality of patients from a respective check item database; (b) selecting a characteristic from the check data of each of the check items; (c) hierarchical fuzzy-inferring the check data from which the characteristic is selected, for each check item; (d) extracting a check opinion for each disease based on an output value of the fuzzy inference for each check item; and (e) making a synthetic clinical estimation based on the extracted check opinion.

Abstract: Disclosed are a planar small electrode sensor for skin impedance measurement and a system using the same. The sensor include: a semiconductor substrate; an insulating layer formed on the substrate; and at least one pair of electrodes which are symmetrically formed on the insulating layer with respect to a vertical central line of the insulating layer, where the at least one pair of electrodes includes a reference electrode and a measuring electrode.

Abstract: A significance parameter extraction method for differential diagnosis of abnormal diseases based on entropy rough approximation technology, including the steps of: (a) calculating clinical reference values from two different groups of clinical data extracted from a database storing a plurality of clinical data for each check item using an entropy maximization measure; (b) evaluating a clinical difference between the two different groups of clinical data and extracting candidate check items; (c) based on a reference value of a check item calculated from one of the groups of clinical data, converting attribute values of the check item into nominal attribute values; and (d) extracting significance parameters for differential diagnosis from the candidate check items extracted in the step (b).