International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.4, No.10, Aug. 2017

Evaluation of Using a Recurrent Neural Network (RNN) and a Fuzzy Logic Controller (FLC) In Closed Loop System to Regulate Blood Glucose for Type-1 Diabetic Patients

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Fayrouz Allam, Zaki Nossair, Hesham Gomma, Ibrahim Ibrahim, Mona Abdelsalam

Index Terms

Type-1 Diabetes;Glucose Contol;RNN;FLC;IOB;Hypo-glycemia;Hyper-glycemia


Type-1 diabetes is a disease characterized by high blood-glucose level. Using a fully automated closed loop control system improves the quality of life for type1 diabetic patients. In this paper, a scalable closed loop blood glucose regulation system which is tuned to each patient is presented. This control system doesn't need any data entry from the patient. A recurrent neural network (RNN) is used as a nonlinear predictor and a fuzzy logic controller (FLC) is used to determine the insulin dosage which is required to regulate the blood glucose level. The insulin infusion is restricted by calculation of insulin on board (IOB) which avoids overdosing of insulin. The performance of the proposed NMPC is evaluated by applying full day meal regime to each patient. The evaluation includes testing in relation to specific life style condition, i.e. fasting, postprandial, fault meal estimation, and exercise as a metabolic disturbance. Our simulation results indicate that, the use of a RNN along with a FLC can decrease the postprandial glucose concentration. The proposed controller can be used in fasting and can avoid severe hypo or hyper-glycemia during fasting. It can also decrease the postprandial glucose concentration and can dynamically respond to different glycemic challenges.

Cite This Paper

Fayrouz Allam, Zaki Nossair, Hesham Gomma, Ibrahim Ibrahim, Mona Abdelsalam,"Evaluation of Using a Recurrent Neural Network (RNN) and a Fuzzy Logic Controller (FLC) In Closed Loop System to Regulate Blood Glucose for Type-1 Diabetic Patients", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.10, pp.58-71, 2012. DOI: 10.5815/ijisa.2012.10.07


[1]Oruklu ME, Cinar A, Quinn L, Smith D. Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. Journal of Process Control 2009; 19: 1333~1346.

[2]Renard E, Costalat G, Chevassus H, Bringer J. Closed loop insulin delivery using implanted insulin pumps and sensors in type 1 diabetic patients. Diabetes Res. Clin. Pract. 2006; 74: S173~S177. 

[3]Lee H, Wayne B. A Closed-loop Artificial Pancreas based on MPC: human-friendly identification and automatic meal disturbance rejection. Proceedings of the 17th World Congress. The International Federation of Automatic Control Seoul 2008; 6~11. 

[4]Clarke WL, Anderson S, Breton M, Patek S, Kashmer L, Kovatchev B. Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience. Journal of Diabetes Science and Technology 2009; 3 (5): 1031~1038.

[5]Rama E, Nagaveni N. Design Methodology of a Fuzzy Knowledgebase System to predict the risk of Diabetic Nephropathy. IJCSI International Journal of Computer Science Issues 2010; 7(5): 1694~0814.

[6]Kalpana M, Kumar AV. Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism. Int. J. Advanced Networking and Applications 2011; 3(2):1128~1134.

[7]Grant P. A new approach to diabetic control: Fuzzy logic and insulin pump technology. Medical Engineering & Physics 2007; 29: 824~827.

[8]IBBINI M. A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients. Journal of Medical Engineering & Technology 2006; 30(2): 83 ~ 92.

[9]Yasini Sh, Naghibi-Sistani MB, Karimpour A. Active Insulin Infusion Using Fuzzy-Based Closed-loop Control. 3rd International Conference on Intelligent System and Knowledge Engineering 2008; 429~434.

[10]Allam F, Nossair Z, Gomma H, Ibrahim I, Abdelsalam M. A Recurrent Neural Network Approach for Predicting Glucose Concentration in Type-1 Diabetic Patients. EANN/AIAI 2011, Part I, IFIP AICT 2011; 363: 254~259.

[11]Allam F, Nossair Z, Gomma H, Ibrahim I, and Abdelsalam Mona. Prediction of Subcutaneous Glucose Concentration for Type-1 Diabetic Patients Using a Feed Forward Neural Network. International Conference on Computer Engineering & Systems (ICCES’2011) 2011; 129-133.

[12]Diabetes Research in Children Network (DirecNet), Mar. 11, 2009, [Online]. Available:

[13]Hovorka R et al.; Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurements 2004; 25(4): 905~920.

[14]Dunn C, Eastman C, Tamada A. Rates of Glucose Change Measured by Blood Glucose Meter and the GlucoWatch Biographer During Day, Night, and Around Mealtimes. DIABETES CARE. 27 (9),September 2004.

[15]Campbell et al., Calculating insulin on board extended bolus being delivered by an insulin delivery device. United States Patent Application Publication, Jan. 21, 2010.

[16]Kovatchev B, Breton M, Dalla Man C, and Cobelli C. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. Journal of Diabetes Science and Technology, 3: (44~85), 2009.

[17]Chassin J, Wilinska E, Hovorka R. Evaluation of glucose controllers in virtual environment: methodology and sample application. Artificial Intelligence in Medicine, 32: 171~ 181, 2004.

[18]Davey G, Roberts J, Patel S, Pierpoint T, Godsland I, Davies B and Mckeigue P M. Effects Of Exercise On Insulin Resistance In South Asians And Europeans. An International Electronic Journal. 3(2), April 2000.