NDLI: Hybrid Progressive Algorithm to Recognize Type II Diabetic Based on Hair Mineral Element Contents

Content Provider	IEEE Xplore Digital Library
Author	Hanming Huang Weiping Hu Zhongzhi Han Hongtao Ye
Copyright Year	2005
Description	Author affiliation: Coll. of Phys. & Inf., Guangxi Normal Univ., Guilin (Hanming Huang)
Abstract	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
Starting Page	4716
Ending Page	4718
File Size	462276
Page Count	3
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615524
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diabetes Hair Minerals Chromium Sampling methods Iron Zinc Information retrieval Immune system Statistical analysis Trace Elements Progressive Algorithm Pattern Classifiers
Content Type	Text

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