(Received: 26-Aug.-2019, Revised: 4-Nov.-2019 and 25-Nov.-2019 , Accepted: 30-Nov.-2019)
Diabetes is one of the most widespread diseases around the world, especially in the western world where non-healthy and fast foods are widely used. Many types of research have been conducted for developing methods for predicting, diagnosing and treating diabetes. One of the tools used for this purpose is mathematical modelling, which is used for developing models of blood glucose and insulin intake. In this paper, a model to determine the proper insulin dose for diabetic inpatients was implemented using Artificial Neural Network (ANN). The model is developed by taking into consideration ten different parameters (Patient's Gender, Patient's Age, Body Mass Index for Patient, Disease History, Total Daily Insulin Doses, Diabetes Type, Smoking Factor, Genetic Factor, Creatinine Clearance and Accumulative Glucose), in addition to real-time blood glucose readings. The model is developed based on a dataset from 159 inpatients from three different hospitals. It was found that the model with the best performance was based on one hidden layer with six neurons and seven inputs. The significant inputs were glucose readouts, glucose difference, normal range, accumulative glucose, history of the disease, total insulin dose and the patient's gender. The MSE of the best model was 5.413 and the correlation was 0.9315 with negligible training time.


  1. Health Organization,[Online], Available:, accessed in Apr. 2019.
  2. World Health Organization–Diabetes Program,[Online], Available: /action_online/basics/en/index.html, accessed in Dec. 2014.
  3. Atlas, Diabetes, "International Diabetes Federation,"[Online], Available:, accessed in Dec. 2014.
  4. C. Klonoff, B. Buse, L. Nielsen, X Guan, L. Bowlus, H. Holcombe, E. Wintle and G. Maggs, ''Exenatide Effects on Diabetes, Obesity, Cardiovascular Risk Factors and Hepatic Biomarkers in Patients with Type 2 Diabetes Treated for at Least 3 Years,'' Current Medical Research and Opinion, vol. 24, no. 1, pp. 275-286, Dec. 2007. 
  5. B. Thomas and V. Tresp, "A Nonlinear State Space Model for the Blood Glucose Metabolism of a Diabetic (Ein nichtlineares Zustandsraummodell für den Blutglukosemetabolismus eines Diabetikers)," at-Automatisierungstechnik, vol. 50, no. 5, pp. 228-236, Sept. 2009.
  6. S. Vashist, D. Zheng, K. Al-Rubeaan, J. Luong and F. Sheu, "Technology behind Commercial Devices for Blood Glucose Monitoring in Diabetes Management: A Review," Analytica Chimica Acta, vol. 703, no. 1, pp. 124–136, Jul. 2011.
  7. H. Park, K. Lee, H. Yoon and H. Nam, ''Design of a Portable Urine Glucose Monitoring System for Health Care,'' Computers in Biology and Medicine, vol. 35, no. 4, pp. 275-286, Apr. 2004.
  8. American Diabetes Association,[Online], Available:, accessed in Apr. 2019.
  9. M. Otoom, H. Alshraideh, H. Almasaeid, D. López-de-Ipiña and José Bravo, ''A Real-time Insulin Injection System," Proceedings of the Ambient Assisted Living and Active Aging- 5th International Work-Conference (IWAAL), pp. 120–127, Dec. 2013.
  10. A. Fidimahery and M. Milgram, "Applying Neural Networks to Adjust Insulin-pump Doses," Proceedings of the 7th IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VII, pp. 182-188, Sept. 1997.
  11. R. DeFronzo, "Insulin Resistance, Lipotoxicity, Type 2 Diabetes and Atherosclerosis: The Missing Links. The Claude Bernard Lecture 2009," Diabetologia, vol. 53, no.7, pp. 1270-1287, Apr. 2010.
  12. T. Shimauchi, N. Kugai, N. Nagata and O. Takatani, ''Microcomputer-aided Insulin Dose Determination in Intensified Conventional Insulin Therapy," IEEE Transactions on Biomedical Engineering, vol. 35, no. 2, pp. 167-171, Feb. 1988.
  13. T. Volker, T. Briegel and J. Moody, "Neural-network Models for the Blood Glucose Metabolism of a Diabetic," IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1204-1213, Sept. 1999.
  14. E. Caballero-Ruiz, G. García-Sáez, M. Rigla, M. Balsells, B. Pons, M. Morillo, E. J. Gómez and M. E. Hernando, ''Automatic Blood Glucose Classification for Gestational Diabetes with Feature Selection: Decision Trees vs. Neural Networks," Proc. of XIII Mediterranean Conference on Medical and Biological Engineering and Computing, pp. 1370-1373, Sept. 2013.
  15. M. Vasudev and J. Johnston, "Inpatient Management of Hyperglycemia and Diabetes," Clinical Diabetes'', vol. 29, no. 1, pp. 3-9, Jan. 2011.
  16. A. Chennakesava, Fuzzy Logic and Neural Networks: Basic Concepts & Applications, India: New Age International, 2008.
  17. H. Simon, Neural Networks: A Comprehensive Foundation, New Jersey: Mc Millan, 2010.
  18. H. Demuth, M. Beale, O. De Jess and M. Hagan, Neural Network Design, 2nd Edition, USA: Martin Hagan, 2014.
  19. S. Milton and J. Arnold, Introduction to Probability and Statistics: Principles and Applications for Engineering and Computing Sciences, USA: McGraw-Hill Education, 2002.
  20. E. Lehmann and G. Casella, Theory of Point Estimation, Berlin: Springer Sci. & Bus. Media, 2006.
  21. Q. Wang, P. Molenaar, S. Harsh, K. Freeman, J. Xie, C. Gold, M. Rovine and J. Ulbrecht, "Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose and Meal Intake: An Extended Kalman Filter Approach," Journal of Diabetes Science and Technology, vol. 8, no. 2, pp. 331-345, March 2014.
  22. S. Pappada, B. Cameron, P. Rosman, R. Bourey, T. Papadimos, W. Olorunto and M. Borst, "Neural Network-based Real-time Prediction of Glucose in Patients with Insulin-dependent Diabetes," Diabetes Technology & Therapeutics, vol. 13, no. 2, pp. 135-141, Feb. 2011.
  23. A. Bani-Younes, Modeling Human Body Responsiveness to Glucose Intake and Insulin Injection Using Neural Networks, Master Thesis, Jordan: Yarmouk University, 2014.
  24. O. Orozco, E. Castañeda, A. Rodrí─▒guez-Herrero, G. García-Saéz and M. Elena Hernando, "Glucose-Insulin Regulator for Type 1 Diabetes Using High-order Neural Networks," International Journal of Artificial Intelligence and Neural Networks (IJAINN), vol. 4, no. 3, pp. 40-47, Sept. 2014.
  25. S. Mougiakakou, A. Prountzou, D. Iliopoulou, K. Nikita, A. Vazeou and C. Bartsocas, "Neural Network Based Glucose-insulin Metabolism Models for Children with Type 1 Diabetes," Proceedings of the 28th IEEE EMBS Annual Int. Conf., New York City, USA, pp. 3545-3548, Aug. 30-Sept. 3, 2006.
  26. G. Robertson, E. D. Lehmann, W. A. Sandham and D. J. Hamilton, "Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-concept Pilot Study," Journal of Electrical and Computer Engineering, vol. 2011, Article ID 681786, pp. 1-11, 2011.
  27. W. A. Sandham, M. Z. Diaz, D. J. Hamilton, E. D. Lehmann, P. Tatti and J. Walsh, "Electrical and Computer Technology for Effective Diabetes Management and Treatment," Special Issue of the Journal of Electrical and Computer Engineering, 2011.
  28. W. A. Sandham, D. J. Hamilton, D. Nikoletou, C. MacGregor, A. Japp and K. Patterson, "Use of Artificial Neural Networks for Improved Diabetes Therapy," Proc. of Irish Signals and Systems Conference (ISSC-98), Dublin Institute of Technology, Dublin, Ireland, pp. 553-560, 25-26 June 1998.
  29. W. A. Sandham, D. Nikoletou, D. J. Hamilton, K. Paterson, A. Japp and C. MacGregor, "Blood Glucose Prediction for Diabetes Therapy Using a Recurrent Artificial Neural Network," Proc. of the IX European Signal Processing Conference (Eusipco-98), Island of Rhodes, Greece, pp. 673-676, 8-11 Sep. 1998.
  30. W. A. Sandham, D. J. Hamilton, A. Japp and K. Patterson, "Neural Network and Neuro-fuzzy Systems for Improving Diabetes Therapy," Proc. of the 20th Int. Conf. of the IEEE Eng. in Med. & Biol. Soc., Hong Kong Convention and Exhibition Centre, Hong Kong, vol. 20, Part 3/6, pp. 1438-1441, 1998.
  31. C. Pérez-Gandía, A. Facchinetti, G. Sparacino, C. Cobelli, E. J. Gómez, M. Rigla, A. de Leiva and M. E. Hernando, "Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring," Diabetes Technology & Therapeutics, vol. 12, no 1, pp.81-88, Jan. 2010.
  32. J.-W. Chen, , K. Li, P. Herrero, T. Zhu and P. Georgiou, "Dilated Recurrent Neural Network for Short-time Prediction of Glucose Concentration," Proc. of the 23rd European Conf. on Artificial Intelligence (IJCAI-ECAI), Int.l Workshop on Knowledge Discovery in Healthcare Data, pp. 69-73, 2018.
  33. K. Li, J. Daniels, C. Liu, P. Herrero-Vinas and P. Georgiou, "Convolutional Recurrent Neural Networks for Glucose Prediction," IEEE Journal of Biomedical and Health Informatics, DOI: 10.1109/JBHI.2019.2908488, Apr. 2019.