Blood glucose prediction using neural network

Chit Siang Soh; Xiqin Zhang; Jianhong Chen; P. Raveendran; Phey Hong Soh; Joon Hock Yeo

doi:10.1117/12.762529

Blood glucose prediction using neural network

Chit Siang Soh, Xiqin Zhang, Jianhong Chen, P. Raveendran, Phey Hong Soh, Joon Hock Yeo

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Advanced Biomedical and Clinical Diagnostic Systems VI
DOIs	https://doi.org/10.1117/12.762529
Publication status	Published - 2008
Externally published	Yes
Event	Advanced Biomedical and Clinical Diagnostic Systems 2008 - San Jose, United States of America Duration: 20 Jan 2008 → 21 Jan 2008 Conference number: 6th https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6848.toc (Proceedings)

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	6848
ISSN (Print)	1605-7422

Conference

Conference	Advanced Biomedical and Clinical Diagnostic Systems 2008
Country/Territory	United States of America
City	San Jose
Period	20/01/08 → 21/01/08
Internet address	https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6848.toc (Proceedings)

Keywords

Blood glucose
Neural network
Non-invasive measurement

Access to Document

10.1117/12.762529

Cite this

@inproceedings{3566bf1008e1468787c7bf9326418795,

title = "Blood glucose prediction using neural network",

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.",

keywords = "Blood glucose, Neural network, Non-invasive measurement",

author = "Soh, {Chit Siang} and Xiqin Zhang and Jianhong Chen and P. Raveendran and Soh, {Phey Hong} and Yeo, {Joon Hock}",

year = "2008",

doi = "10.1117/12.762529",

language = "English",

isbn = "9780819470232",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

booktitle = "Advanced Biomedical and Clinical Diagnostic Systems VI",

note = "Advanced Biomedical and Clinical Diagnostic Systems 2008 ; Conference date: 20-01-2008 Through 21-01-2008",

url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6848.toc",

}

Soh, CS, Zhang, X, Chen, J, Raveendran, P, Soh, PH & Yeo, JH 2008, Blood glucose prediction using neural network. in Advanced Biomedical and Clinical Diagnostic Systems VI. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 6848, Advanced Biomedical and Clinical Diagnostic Systems 2008, San Jose, California, United States of America, 20/01/08. https://doi.org/10.1117/12.762529

TY - GEN

T1 - Blood glucose prediction using neural network

AU - Soh, Chit Siang

AU - Zhang, Xiqin

AU - Chen, Jianhong

AU - Raveendran, P.

AU - Soh, Phey Hong

AU - Yeo, Joon Hock

N1 - Conference code: 6th

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Blood glucose

KW - Neural network

KW - Non-invasive measurement

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U2 - 10.1117/12.762529

DO - 10.1117/12.762529

M3 - Conference Paper

AN - SCOPUS:42149121563

SN - 9780819470232

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Advanced Biomedical and Clinical Diagnostic Systems VI

T2 - Advanced Biomedical and Clinical Diagnostic Systems 2008

Y2 - 20 January 2008 through 21 January 2008

ER -

Blood glucose prediction using neural network

Abstract

Publication series

Conference

Keywords

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