# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 7 Conclusions and Future Works  
In this paper, a non-invasive blood glucose meter based on multi-wavelength absorbance is studied. To deal with the individual discrepancies (IDs), a learning algorithm has been proposed, along with a device prototype. The experiments demonstrate that the correlation coefficient between measuring results of the prototype and an invasive glucose meter can reach 0.5 after merely 10 times of parameters correction, not to mention that after 60 times of correction the correlation coefficient rises to 0.80, which means that if you measure blood glucose 7 times a day, it only takes about 8 days to perform invasive calibrations, and then you can perform non-invasive measurement. This kind of glucose meter is able to reduce the effect of IDs in optical measurement method. In other words, in order to eliminate the impact of IDs, the model parameters of this type of learning glucose meter are individually customized. This type of glucose meter also has a shortcoming, i.e., it cannot be used interchangeably, since the parameters of a meter are adjusted through an individual training dataset. Compared with other NGM methods which require high-precision instruments, this learning non-invasive glucose meter has a lower hardware cost at only about $50, which will lead to a higher market prospect. In summary, the main contributions of this paper are as follows:  
* We have conducted a survey on the FDA or CE certified mini-invasive or non-invasive glucose meters currently available on the market;
* For non-invasive blood glucose modeling, we propose a learning model framework, including an independent feature converter (IFC) and a glucose estimator (GE);
* Based on our model, we designed a prototype of a non-invasive blood glucose meter, and collected real-world data with it;
* Through experimental verification, we found that the combination of ICA and RF has the highest accuracy for estimating blood glucose concentration, and ICA helps to speed up the convergence of the model;
* We confirmed the existence of individual discrepancies in NGM through the transplant experiment, and verified that the glucose meters based on a learning model cannot be used interchangeably.  
This paper is only a preliminary/exploratory study of non-invasive blood glucose measurement techniques due to the efficiency and performance of our device and algorithm were evaluated on only 6 subjects. In the future, we will improve our equipment design and conduct clinical trails with more subjects and longer durations. The technologies of NGM has been developing rapidly in recent years, but most of them are in the lab-only stage, and there is still a long way to go for clinical use.  
Conceptualization, W.L. and A.H.; Methodology, W.L.; Hardware and software, W.L. and P.W.; Resources, A.H. and P.W.; Data curation, W.L. and A.H.; Writing--original draft preparation, W.L.; Writing--review and editing, A.H. and P.W.; Visualization, W.L.; Project administration, P.W.; Funding acquisition, P.W.  
This research was funded by Peking University Medical Cross Research Seed Fund.  
**Acknowledgments:** Our special thanks go to all the subjects who participated in the data acquisition.  
**Conflicts of Interest:** The authors declare no conflict of interest.