Proposal of Prediction Accuracy Improvement in Non-invasive Blood Glucose Measurement using MHC Method
According to the World Health Organization, 422 million adults worldwide have diabetes. Diabetic patients must regularly measure and manage their blood glucose levels. However, existing blood glucose meters require needles to draw blood, causing pain and infection problems. The measuring instrument to solve this problem would be a non-invasive blood glucose measuring instrument, but none is practically used at present. The authors have been studying non-invasive blood glucose measurement methods to reduce the burden on diabetic patients. As a result of previous research, we developed a simple non-invasive blood glucose meter and a blood glucose control system. Currently, our aim is to improve accuracy of blood glucose level prediction. In this paper we report on the blood glucose level prediction technique realized by using biological information. As a result, heart rate and MHC method are used as input data, and prediction using machine learning can be performed with high accuracy.
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