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Proceedings Paper

Blood glucose prediction using neural network
Author(s): Chit Siang Soh; Xiqin Zhang; Jianhong Chen; P. Raveendran; Phey Hong Soh; Joon Hock Yeo
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Paper Abstract

We used neural network for blood glucose level determination in this study. The data set used in this study was collected using a non-invasive blood glucose monitoring system with six laser diodes, each laser diode operating at distinct near infrared wavelength between 1500nm and 1800nm. The neural network is specifically used to determine blood glucose level of one individual who participated in an oral glucose tolerance test (OGTT) session. Partial least squares regression is also used for blood glucose level determination for the purpose of comparison with the neural network model. The neural network model performs better in the prediction of blood glucose level as compared with the partial least squares model.

Paper Details

Date Published: 5 March 2008
PDF: 5 pages
Proc. SPIE 6848, Advanced Biomedical and Clinical Diagnostic Systems VI, 68480B (5 March 2008); doi: 10.1117/12.762529
Show Author Affiliations
Chit Siang Soh, Univ. Malaya (Malaysia)
Xiqin Zhang, Glucostats System (Singapore)
Jianhong Chen, Nanyang Technological Univ. (Singapore)
P. Raveendran, Univ. Malaya (Malaysia)
Phey Hong Soh, Nanyang Technological Univ. (Singapore)
Joon Hock Yeo, Nanyang Technological Univ. (Singapore)

Published in SPIE Proceedings Vol. 6848:
Advanced Biomedical and Clinical Diagnostic Systems VI
Tuan Vo-Dinh; Warren S. Grundfest; David A. Benaron; Gerald E. Cohn, Editor(s)

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