Algorithm for Predicting the Glicemic Profil in Diabetes Under Regular Measurements


  • Svitlana Kiforenko International Research and Training Center for Information Technologies and Systems, NASU and MESU, Ukraine
  • Igor Vasyliev Taras Shevchenko National University of Kyiv, Ukraine
  • Mykola Lavrenyuk Taras Shevchenko National University of Kyiv, Ukraine
  • Tatiana Hontar International Research and Training Center for Information Technologies and Systems, NASU and MESU, Ukraine



diabetes, glycemic regulation, mathematical modeling, identification method, forecasting algorithm, software and algorithmic structure, computer simulation research


Background. In recent years, modern technical devices have been created so that to use in the practice of treating diabetes mellitus. These are systems for continuous monitoring of glycemia, which is a significant addition to the widely accepted measurements of glucose levels with a glucometer, various infusion systems, which significantly improve the doctor's decision-making process. However, such technical means are quite expensive and inaccessible to a wide range of users. In addition, their use is associated with both adverse reactions when wearing them and with patient compliance issues. In this case an alternative can be using mathematical modeling tools.

Objective. The aim of the paper is to prove the possibility of using mathematical modeling to predict the glycemic profile as a certain degree of alternative to a sensor for continuous monitoring of blood glucose levels under conditions of limited irregular measurements.

Methods. To solve the problem it is proposed to employ the technology of mathematical modeling. The structure of the model makes it possible to implement the mathematical formalism by analytical formulae.

Results. As a result, the insulin-glucose-tolerance test has been developed that allows quantitatively assessing a patient's personal sensitivity to insulin-bolus therapy. We proposed the mathematical model for solving the problem by analytical formulae. Algorithms for identifying model parameters, an algorithm for calculating the insulin dose that compensates for the carbohydrate component in the intended meal, and an algorithm for predicting the daily glycemic profile have been developed. The software-algorithmic structure for the implementation of the mathematical formalism has been developed.

Conclusions. The conducted simulation study employing the technology of mathematical modeling makes it possible to evaluate the functioning of the developed procedures at the preclinical stage. The simplicity of calculations using analytical formulae can be a prerequisite for the implementation of the algorithm in portable autonomous special-purpose devices or in smartdata under the Android OS, which is a definite contribution to development of digital diabetology.


Karpel’ev VA, Filippov YI, Tarasov YV, Boyarsky MD, Mayorov AY, Shestakova MV, et al. Mathematical modeling of the blood glucose regulation system in diabetes mellitus patients. Vestn Ross Akad Med Nauk. 2015;(5):549-60. DOI: 10.15690/vramn.v70.i5.1441

Cobelli C, Dalla Man C, Sparacino G, Magni L, De Nicolao G, Kovatchev BP. Diabetes: models, signals, and control. IEEE Rev Biomed Eng. 2009 Jan 1;2:54-96. DOI: 10.1109/RBME.2009.2036073

Kiforenko SI. Hierarchical modeling – the basis of technology of preclinical testing of glycemic level control algorithms. Cybern Com Eng J. 2017;1:80-96. DOI: 10.15407/kvt187.01.080

Sokol EI, Lapta SS. Mathematical model for the regulation of carbohydrate metabolism. Vestnik NTU KHPI. 2015;33:152-7.

Gomenyuk SM, Emel'yanov AO, Karpenko AP, Chernezov SA. Review of optimal insulin dose forecasting methods and systems for insulin-dependant diabetes patients. Inform Technol. 2010;3;48-57. DOI: 10.7463/0409.0119663

Cappon G, Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics. 2017;6(3):65. DOI: 10.3390/electronics6030065

Vettoretti M, Facchinetti, A. Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review. Biomed Eng Online. 2019 Mar 29;18(1):37. DOI: 10.1186/s12938-019-0658-x

Rodbard D. Continuous glucose monitoring: A review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther. 2017;19(S3):S25-37. DOI: 10.1089/dia.2017.0035

Heinemann L, DeVries JH. Reimbursement for continuous glucose monitoring. Diabetes Technol Ther. 2016;18(Suppl 2):S248-52. DOI: 10.1089/dia.2015.0296

Heinemann L, Franc S, Phillip M, Battelino T, Ampudia-Blasco FJ, Bolinder J, et al. Reimbursement for continuous glucose monitoring: A European view. J Diabetes Sci Technol. 2012 Nov 1;6(6):1498-502. DOI: 10.1177/193229681200600631

Bolie V. Coefficients оf normal blood glucose regulation. J Appl Physiol. 1961;16:783-8. DOI: 10.1152/jappl.1961.16.5.783

Palumbo P, Ditlevsen S, Bertuzzi A, De Gaetano A. Mathematical modeling of the glucose–insulin system: A review. Math Biosci. 2013;244(2):69-81. DOI: 10.1016/j.mbs.2013.05.006

Nefedov VP, Yasaytis AA, Novoseltsev VN. Homeostasis at different levels of the organization of biosystems. Novosibirsk: Nauka; 1991. 232 p.

Gavaghan D, Garny A, Maini PK, Kohl P. Mathematical models in physiology. Philos Trans A Math Phys Eng Sci. 2006 May 15;364(1842):1099-106. DOI: 10.1098/rsta.2006.1757

Cobelli C, Carson ER, Finkelstein L, Leaning MS. Validation of simple and complex models in physiology and medicine. Am J Physiol. 1984 Feb;246(2 Pt 2):R259-66. DOI: 10.1152/ajpregu.1984.246.2.R259

Cobelli C, Carson ER. Introduction to modeling in physiology and medicine. New York: Elsevier/Academic Press; 2008. 328 p.

Cobelli C, Federspil G, Pacini G, Salvan A, Scandellari C. An integrated mathematical model of the dynamics of blood glucose and its hormonal control. Math Biosci.1982;58(1):27-60. DOI: 10.1016/0025-5564(82)90050-5

Lehmann ED, Chatu SS, Hashmy SS. Retrospective pilot feedback survey of 200 users of the AIDA Version 4 Educational Diabetes Program. 1–Quantitative survey data. Diabetes Technol Ther. 2006 Jun;8(3):419-32. DOI: 10.1089/dia.2006.8.419

Rutscher A, Salzsieder E, Thierbach U, Fischer U, Albrecht G. Kadis–A computer-aided decision support system for im-proving the management of type-1 diabetes. Exp Clin Endocrinol. 1990 Feb;95(1):137-47. DOI: 10.1055/s-0029-1210946

Dartau LA, Orkina EL, Novoseltsev VN. Carbohydrate metabolism: Minimal models and management. Engineering physio¬logy and modeling of body systems. Novosibirsk: Nauka; 1987. p. 70-5.

Cobelli C, Man CD, Pedersen MG, Bertoldo A, Toffolo G. Advancing our understanding of the glucose system via modeling: A perspective. IEEE Trans Biomed Eng. 2014 May;61(5):1577-92. DOI: 10.1109/TBME.2014.2310514

Lapta SS, Pospelov LA, Solovieva OI. Computerized early diagnosis of diabetes mellitus by methods of mathematical mo-deling. Vestnik NTU KHPI. 2014;36:55-61.

Kovatchev BP, Breton MD, Dalla Man C, Cobelli C. In silico model and computer simulation environment approximating the human glucose/insulin utilization. Food and Drug Administration Master File MAF1521. 2008.

Kovatchev BP, Breton M, Man CD, Cobelli C. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol. 2009;3(1):44-55. DOI: 10.1177/193229680900300106

Dalla Man C, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C. The UVA/PADOVA type 1 diabetes simulator: New features. J Diabetes Sci Technol. 2014 Jan;8(1):26-34. DOI: 10.1177/1932296813514502

Kapuro J, Sheable T, McCan T. Method and system for controlling the tuning factor in connection with the replacement of a sensor for a feedback controller with an artificial pancreas [Internet]. 2021 [cited 2020 Dec 7]. Available from:



How to Cite

Kiforenko S, Vasyliev I, Lavrenyuk M, Hontar T. Algorithm for Predicting the Glicemic Profil in Diabetes Under Regular Measurements. Innov Biosyst Bioeng [Internet]. 2021Apr.6 [cited 2023May30];5(1):17-26. Available from: