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

Ahmed, Y. B. and Mahmud, A. E., 2013, A Fuzzy Controller for Blood Glucose-Insulin System. Journal of Signal and Information Processing, 4(2):111-117.10.4236/jsip.2013.42015Search in Google Scholar

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.Search in Google Scholar

Bremer, T. and Gough, D. A., 1999, Is Blood Glucose Predictable from Previous Values?. Diabetes, 48(3):445–451.10.2337/diabetes.48.3.44510078542Open DOISearch in Google Scholar

Charles, D. C., 2002, Pharmacology in Rehabilitation. Pennsylvania: F.A. Davis.Search in Google Scholar

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):195-214.10.1016/0169-2607(90)90102-FSearch in Google Scholar

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

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):142.10.1007/s10916-017-0788-2Open DOISearch in Google Scholar

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

International Diabetes Federation, 2016, IDF Diabetes Atlas – 7^th Edition. Retrieved September 2, 2016, from http://www.idf.org/diabetesatlas.Search in Google Scholar

Juan, Li. and Chandima, F., 2016, Smartphone-based personalized blood glucose prediction, ICT Express, 2(4):150-154.10.1016/j.icte.2016.10.001Search in Google Scholar

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, 771-774.10.1007/978-981-10-5122-7_193Search in Google Scholar

Mathers, C. D. and Loncar, D., 2006, Projections of global mortality and burden of disease from 2002 to 2030, PLoS Medicine, 3(11):e442.10.1371/journal.pmed.0030442Search in Google Scholar

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.Search in Google Scholar

Michael, C. R. and Bruce, A. P., 2006. Type 1 Diabetes and Vigorous Exercise: Applications of Exercise Physiology to Patient Management. Canadian Journal of Diabetes, 30(1):63-71.10.1016/S1499-2671(06)01010-0Search in Google Scholar

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.Search in Google Scholar

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):1-5.10.1186/1754-9493-4-15294419420828400Search in Google Scholar

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.Search in Google Scholar

Shoback, D., 2011, Greenspan's Basic & Clinical Endocrinology. (9th ed.). New York: McGraw-Hill Medical.Search in Google Scholar

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):931–937.10.1109/TBME.2006.88977417518291Open DOISearch in Google Scholar

Stahl, F., and Johansson, R., 2009, Diabetes Mellitus Modelling and Short term Prediction based on Blood Glucose Measurements. Mathematical Biosciences, 217(2):101-117.10.1016/j.mbs.2008.10.00819022264Search in Google Scholar

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/Search in Google Scholar

World Health Organization, 2014, Global Health Estimates: Deaths by Cause, Age, Sex and Country, 2000-2012. Geneva, Retrieved September2,2016, from https://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.htmlSearch in Google Scholar

World Health Organization, 2013, World Health Organization. Retrieved March 5, 2014 from http://www.who.int/mediacentre/factsheets/fs312/en/Search in Google Scholar

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.Search in Google Scholar