Relationship between statistics and filters in noninvasive blood glucose estimation analysis

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.