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Multi-level Fuzzy Inference using Hair Tissue Mineral Analysis for Estimating Diagnoses


Young Im Cho, Sanggil Kang


Vol. 8  No. 9  pp. 42-50


In this paper we introduce a multi-level fuzzy inference system using hair tissue mineral analysis (HTMA) for estimating diagnoses. In HTMA, the characteristics of mineral ratios can be represented in linguistic terms of the metabolic characteristics and nutritional requirements. Thus the mechanism can be expressed in fuzzy sets, i.e., ‘high’, ‘low’, and ‘acceptable.’ Based on the fuzzy sets of mineral ratios, we can build fuzzy rules between the mineral ratios and doctors’ diagnosis reports. Conventional fuzzy inference system can not be suitable for the hair tissue mineral analysis because a lot of mineral examination is needed to make various diagnoses for a patient. It causes fuzzy inference to be complicated because of increasing the combination of rules. To solve the problem, we introduce a multi-level fuzzy inference system which breaks down the fuzzy rules used for estimating a diagnosis into basic units of fuzzy rules which are connected in a chain form according to the premise and conclusion in the basic units. By using the multi-level fuzzy rules, we can infer a diagnosis without whole mineral ratios required for estimating diagnoses. In the experimental section, we showed the efficiency of our method by comparing with the conventional method using 500 patients’ hair tissue mineral analysis information.


Diagnosis, fuzzy set, multi-level fuzzy inference system, TMA.