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Cybernetics and programming

Neural Network Model for Diabetes Patients Blood Glucose Prediction

Mustafaev Arslan Gasanovich

Doctor of Technical Science

Professor of the Department "Information technologies and information security" of the Dagestan State University of National Economy

367015, Russia, respublika Dagestan, g. Makhachkala, ul. Ataeva, 5, kab. 4.5

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Abstract: Diabetus melitus is a metabolic disorder caused by an absolute deficiency of insulin secretion and characterized by the inability of the body to maintain an adequate blood glucose level. Optimal doses and types of artificial insulin depend on many factors. In this paper the author proposes a neural network model of blood glucose prediction allowing to predict impending critical condition of patients suffering from diabetes. Implementation of the prediction system combined with an insulin pump creates a range of opportunitities for constructing a system of automatic blood glucose level control. The simulation was performed by using The Neural Network Toolbox of the Matlab 2015b environment due to a wide range of opportunities offered by the system, convenience of developing compex applications, advanced imaging study results. The results of the training process and verification of the proposed prediction model performance show that artificial neural networks of direct distribution can help to sustain a satisfactory blood glucose level at all stages of prediction. The average quadratic prediction error did not exceed 3 in the process of the research.  

Keywords: continuous glucose monitoring, supervised learning, feedforward network, data classification, predicting, back propagation of error, multilayer perceptron, artificial neural network, glucose level, diabetes mellitus
This article written in Russian. You can find full text of article in Russian here .

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