Diabetes is a chronic metabolic disease. Due to insufficient insulin secretion to control blood glucose or the inability of the body to effectively use insulin, the blood glucose of patients will be higher than the normal value, resulting in various complications, which will seriously affect the health of patients. Real-time monitoring of blood glucose levels is crucial for early screening of high incidence of diabetes, as well as for diagnosis and treatment of patients with diabetes. Is proposed in this paper in the near-infrared (NIR) the application of noninvasive blood glucose level prediction, analyses the statistical characteristics and the relationship between the filter, proposed the concept of some new characteristics of filter, the filter is applied to the analysis of near infrared non-invasive blood glucose estimates, experimental results show that the new features in the machine learning model can improve the effect of the model.

In the past few decades, due to the increasing emphasis on health, blood glucose, a healthy reference value, has received more and more attention. Traditional invasive blood glucose testing methods require pricking a finger to take a drop of blood, and measuring blood glucose levels based on how the device reacts with the blood. Due to various shortcomings of traditional methods, and the semi-invasive or minimally invasive blood glucose monitoring systems that have been marketed in many countries and regions have high costs and some usage limitations, a new type of easy-to-use non-invasive blood glucose detection and prediction system is rapidly developing. This paper introduces a wearable non-invasive blood glucose detection device using near-infrared technology and its data processing technology, which includes extracting features from the obtained signals and using machine learning methods for blood glucose level prediction, and novel use of the solution The optimization problem of different norm values is used to obtain new statistical features to further improve the accuracy of non-invasive blood glucose prediction.