Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep-learning method

Abstract Extracting micro-scale spectral features from dynamic blood glucose concentrations is extremely difficult when using non-invasive measurement methods. This work proposes a new machine-learning method based on near-infrared spectroscopy, a deep belief network (DBN), and a support vector machine to improve prediction accuracy. First, the standard oral glucose tolerance test was used to collect near-infrared spectroscopy and actual blood glucose concentration values for specific wavelengths (1200, 1300, 1350, 1450, 1600, 1610, and 1650 nm); the blood glucose concentrations were within a clinical range of 70 ∼ 220 mg dl −1 . Second, based on the DBN model, high-dimensional deep features of the non-invasive blood glucose spectrum were extracted. These were used to establish a support vector regression (SVR) model and to quantitatively analyze the influence of the spectral sample size and corresponding feature dimensions (i.e. DBN structure) on prediction accuracy. Finally, based on data from six volunteers, a comparative analysis of the SVR model’s prediction accuracy was performed both before and after using high-dimensional deep features. For volunteer 1, when the DBN-based high-dimensional deep features were used, the root mean square error of the SVR model was reduced by 71.67%, and the correlation coefficient ( R 2 ) and the P value of the Clark grid analysis ( P ) were increased by 13.99% and 6.28%, respectively. Moreover, we had similar results when the proposed method was carried out on the data of other volunteers. The results show that the presented algorithm can play an important role in dynamic non-invasive blood glucose concentration prediction and can effectively improve the accuracy of the SVR model. Further, by applying the algorithm to six independent sets of data, this research also illustrates the high-precision regression and generalization capabilities of the DBN-SVR algorithm.