Paper
5 March 2008 Blood glucose prediction using neural network
Chit Siang Soh, Xiqin Zhang, Jianhong Chen, P. Raveendran, Phey Hong Soh, Joon Hock Yeo
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chit Siang Soh, Xiqin Zhang, Jianhong Chen, P. Raveendran, Phey Hong Soh, and Joon Hock Yeo "Blood glucose prediction using neural network", Proc. SPIE 6848, Advanced Biomedical and Clinical Diagnostic Systems VI, 68480B (5 March 2008); https://doi.org/10.1117/12.762529
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Cited by 1 scholarly publication.
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KEYWORDS
Glucose

Blood

Neural networks

Near infrared

Semiconductor lasers

Data modeling

Laser systems engineering

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