Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25090
Title: Diabetes identification and classification by means of a breath analysis system
Authors: Guo, D
Zhang, D 
Li, N
Zhang, L 
Yang, J
Issue Date: 2010
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2010, v. 6165 LNCS, p. 52-63 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: This article proposes a breath analysis system that makes use of chemical sensors to detect acetone in human breath, and hence detect the diabetes and measure the blood glucose levels of diabetics. We captured the breath samples from healthy persons and patients known to be afflicted with diabetes and conducted experiments on disease identification and simultaneous blood glucose measurement. SVM classifier was used to identify diabetes from healthy samples and three models were built to fit the curves that can represent the blood glucose levels. The results show that the system is not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood sugar of diabetics and therefore to be an evaluation tool for monitoring the blood glucose of diabetes.
Description: 2nd International Conference on Medical Biometrics, ICMB 2010, Hong Kong, 28-30 June 2010
URI: http://hdl.handle.net/10397/25090
ISBN: 3642139221
9783642139222
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-13923-9_6
Appears in Collections:Conference Paper

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