Comparison of Multi-Factor-Considered Blood Glucose Prediction Models by Near-Infrared Spectroscopy

Xiaofei Wang; Xinyi Zhang; Xinhe Xu

doi:10.3788/LOP56.041701

Laser & Optoelectronics Progress, Volume. 56, Issue 4, 041701(2019)

Comparison of Multi-Factor-Considered Blood Glucose Prediction Models by Near-Infrared Spectroscopy

Xiaofei Wang^*, Xinyi Zhang, and Xinhe Xu

School of Instrumentation Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing 100192, China

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Figures & Tables(6)

Fig. 1. Experiment scheme for measuring blood glucose

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Fig. 2. RBF modeling results with non-measurement-component considered. (a) Predicted values and true values; (b) relative errors

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Fig. 3. Linear kernel function modeling results with non-measurement-component considered. (a) Predicted values and true values; (b) relative errors

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Fig. 4. Modeling results without non-measurement-component considered. (a) Predicted values and true values; (b) relative errors

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Fig. 5. Relative errors of SVM model

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Table 1. Model parameters
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Table 1. Model parameters
Modeling Training set Prediction set
R RMSEC R RMSEP
With non-measurement-component considered 0.9993 0.02 0.9627 0.13
Without non-measurement-component considered 0.9344 0.17 0.8655 0.23

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Xiaofei Wang, Xinyi Zhang, Xinhe Xu. Comparison of Multi-Factor-Considered Blood Glucose Prediction Models by Near-Infrared Spectroscopy[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041701

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