In this paper, a hybrid progressive algorithm to recognize type II diabetic based on hair mineral element levels is proposed. Hair samples of 244 cases (Table 1) are collected from 51 healthy persons (one case each person), 47 unchecked diabetics (one case each person) and 73 checked diabetics (two cases each person). 8 hair elements (Mg, Ca, Fe, Cu, Zn, Se, Cr and Mn) are measured. The hybrid progressive algorithm is used to form a scalar quantity (dynamic diagnosis index (DDI)) based hair element levels. The result show that hair may be a good symptom index to judge whether a person affected by diabetes mellitus if appropriate sampling and measuring procedure adopted and proper algorithm to retrieve information from multi-elements levels in hair. Because the non-invasive characteristics of hair analysis, this procedure and algorithm is very suitable at least to large population screening of early diabetes.
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