A blood glucose prediction system by chaos approach

For suppressing the development of diabetes mellitus and the onset of complications, an insulin therapy has been used for suppressing and normalizing the change of a blood glucose. In a blood glucose control by linear method such as conventional ARMA, however, there exists problem that results in the frequency of hypoglycemia. In a blood glucose prediction by a chaos theory, there also exists problem that results in the lower accuracy on behalf of the impossibility in the long-time prediction. For the improvement in the prediction accuracy of the blood glucose that looks like complicated time series, we propose a system combining the deterministic chaos theory using equal time interval, local fuzzy reconstruction method, and minimal linear model. By local fuzzy reconstruction method, we can predict the fasting blood glucose in the short term and then we can estimate the appropriate amount of insulin shot based on the measured bedtime blood glucose. Using the system, the change of blood glucose can be suppressed and normalized and the number of the insulin dosage a day can be reduced to once. Here we report the high effective result of applying the system to type II diabetes mellitus patient.