A Coupled Insulin and Meal Effect Neuro-Fuzzy Model for The Prediction of Blood Glucose Level in Type 1 Diabetes Mellitus Patients.

Open access


Diabetes Mellitus is a metabolic disorder that affects the ability of the human body to properly utilize and regulate glucose. It is pervasive world-wide yet tenuous and costly to manage. Diabetes Mellitus is also difficult to model because it is nonlinear, dynamic and laden with mostly patient specific uncertainties. A neuro-fuzzy model for the prediction of blood glucose level in Type 1 diabetic patients using coupled insulin and meal effects is developed. This study establishes that the necessary and sufficient conditions to predict blood glucose level in a Type 1 diabetes mellitus patient are: knowledge of the patient’s insulin effects and meal effects under diverse metabolic scenarios and the transparent coupling of the insulin and meal effects. The neuro-fuzzy models were trained with data collected from a single Type 1 diabetic patient covering a period of two months. Clarke’s Error Grid Analysis (CEGA) of the model shows that 87.5% of the predictions fall into region A, while the remaining 12.5% of the predictions fall into region B within a four (4) hour prediction window. The model reveals significant variation in insulin and glucose responses as the Body Mass Index (BMI) of the patient changes.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Ahmed Y. B. and Mahmud A. E. 2013 A Fuzzy Controller for Blood Glucose-Insulin System. Journal of Signal and Information Processing 4(2):111-117.

  • American Council of Exercise 2013 Exercise and Type I Diabetes. Fit Facts Retrieved March 5 2015 from http://wellnessproposals.com/fitness/handouts/healthchallenges/exercise_diabetes.pdf.

  • Bremer T. and Gough D. A. 1999 Is Blood Glucose Predictable from Previous Values?. Diabetes 48(3):445–451.

  • Charles D. C. 2002 Pharmacology in Rehabilitation. Pennsylvania: F.A. Davis.

  • Deutsch T. Carson E.R. Harvey E. Lehmann E.D. Sonksen P.H. Tamas G. Whitney K. and Williams C.D. 1990 Computer assisted diabetes management A complex approach. Computer Methods and Programs in Biomedicine 32(3-4):195-214.

  • Ghevondian N. Nguyen H. T. and Colagiuri S. 2001 A Novel Fuzzy Neural Network Estimator for Predicting Hypoglycemia in Insulin- Induced Subjects. Proceedings- 23rd Annual Conference - IEEE/EMVS 1657-1657.

  • Hidalgo J. I. Colmenar J. M. Kronberger G. Winkler S. M. Garnica O. and Lanchares J. 2017 Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods Journal of Medical Systems 41(9):142.

  • International Diabetes Federation 2013 IDF Diabetes Atlas – 6th Edition Brussels Belgium Retrieved September 2 2016 from http://www.idf.org/diabetesatlas/data-visualisations.

  • International Diabetes Federation 2016 IDF Diabetes Atlas – 7th Edition. Retrieved September 2 2016 from http://www.idf.org/diabetesatlas.

  • Juan Li. and Chandima F. 2016 Smartphone-based personalized blood glucose prediction ICT Express 2(4):150-154.

  • Kyriaki S. Martin M. Katerina S. Pavlína P. and Lenka L. 2018 Predicting Blood Glucose Levels for a Type I Diabetes Patient by Combination of Autoregressive with One Compartment Open Model IFMBE proceedings 771-774.

  • Mathers C. D. and Loncar D. 2006 Projections of global mortality and burden of disease from 2002 to 2030 PLoS Medicine 3(11):e442.

  • Melissa C. S. 2012 Diabetes (Type 1 and Type 2). Medicine Net. Retrieved February 3 2014 from http://www.medicinenet.com/diabetes_mellitus/page4.htm.

  • Michael C. R. and Bruce A. P. 2006. Type 1 Diabetes and Vigorous Exercise: Applications of Exercise Physiology to Patient Management. Canadian Journal of Diabetes30(1):63-71.

  • Moshe P. Tadej B. Eran A.Olga K. Natasa B. Shahar M.Torben B. Magdalena A. Stefanija M.D. Ido M. Revital N. and Thomas D. 2013 Artificial Pancreas for Nocturnal Glucose Control. The New England Journal of Medicine N ENGL J MED 368(9):824 – 833.

  • Scott M. P. Marilyn J. B. Brent D. C. Raymond E. B. Jason D. L. Desmond S. Antonio C. and Thomas J. P. 2010 Development of a neural network model for predicting glucose levels in a surgical critical care setting. Patient Safety in Surgery Journal 4(15):1-5.

  • Shanthi S. Kumar D. Varatharaj S. and Santhana S. 2010 Prediction of Hypo/Hyperglycemia through System Identification Modelling and Regularization of Ill- Posed Data. International Journal of Computer Science & Emerging Technologies 1(4):171 – 176.

  • Shoback D. 2011 Greenspan's Basic & Clinical Endocrinology. (9th ed.). New York: McGraw-Hill Medical.

  • Sparacino G. Zanderigo F. Corazza S. Maran A. Facchinetti A. and Cobelli C. 2007 Glucose Concentration can be Predicted Ahead in Time from Continuous Glucose Monitoring Sensor Time-Series. IEEE Trans. Biomed.Eng. 54(5):931–937.

  • Stahl F. and Johansson R. 2009 Diabetes Mellitus Modelling and Short term Prediction based on Blood Glucose Measurements. Mathematical Biosciences 217(2):101-117.

  • World Health Organization 2011. Global status report on noncommunicable diseases 2010 Geneva Retrieved September 2 2016 from https://www.who.int/nmh/publications/ncd_report2010/en/

  • World Health Organization 2014 Global Health Estimates: Deaths by Cause Age Sex and Country 2000-2012. Geneva Retrieved September22016 from https://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html

  • World Health Organization 2013 World Health Organization. Retrieved March 5 2014 from http://www.who.int/mediacentre/factsheets/fs312/en/

  • Zarita Z. Ong P. and Cemal A. 2009. A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients. International Journal of Information and Mathematical Sciences 5(1):72 - 79.

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 170 171 18
PDF Downloads 132 132 14