MULTI-FACTOR NEURAL NETWORK MODEL OF SHORT-TERM PREDICTION OF GLYCEMIA IN PATIENTS WITH TYPE I DIABETES
The problem of developing prognostic models that are embedded in the algorithms of the artificial pancreas (IRE), a device that automatically controls glycemia, is considered. This article is devoted to the study of the effectiveness of using neural network algorithms for predicting blood glucose values based on three main factors: the current level of glycemia, blood glucose and blood carbohydrates. The training of neural network models was carried out on the biometric data of real patients obtained from sensors installed on the body of the subjects. A direct distribution neural network, a multilayer perceptron, was defined as a model. The neural network model of short-term glucose forecasting considered in this work based on three external factors has good approximation properties and can be used as a prognostic model in the IPI program block.