Knowledge Acquisition for an Expert System for Diabetic

  • Ibrahim Mohamed Ahmed Ali
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)


Diabetes is a serious health problem today. Most of the people are unaware that they are in risk of or may even have type-2 diabetes. Type-2 diabetes is becoming more common due to risk factors like older age, obesity, lack of exercise, family history of diabetes, heart diseases. Along with good lifestyle and healthy diet, reduces the risk of development of type 2 diabetes for treatment of elder people, proper care of diet, exercise and medication as well is more important. The research in developing intelligence knowledge base systems in diabetic domain is important for both health industry and diabetes patients. Recently expert systems technology provides an efficient tools for diagnosing diabetes and hence providing a sufficient treatment. The main challenge in building such systems is the knowledge acquisition and development of the knowledge base of these systems. Our research was motivated by the need of such an efficient tool. This paper presents the knowledge acquisition process for developing the knowledge base of diabetic type-2 diet.


Expert systems Knowledge representation Diabetic diet Type 2 diabetes Rule-base 

CCS Concepts

Artificial intelligence Knowledge engineering 


  1. 1.
    Yang, H.H., Miller, S.: A PHP-CLIPS Based Intelligent System for Diabetic Self-diagnosis. Department of Math & Computer Science, Virginia State University Petersburg (2006)Google Scholar
  2. 2.
    Shortliffe, E.H., Perreault, L.E. (eds.): Medical Informatics: Computer Applications in Health Care and Biomedicine. Springer, New York (2001)Google Scholar
  3. 3.
    Federal Bureau of Prisons Management of Diabetes Clinical Practice Guidelines June (2012)Google Scholar
  4. 4.
    Forbes, D., Wongthongtham, P., Singh, J.: Development of Patient-Practitioner Assistive Communications (PPAC) Ontology for Type 2 Diabetes Management. Curtin University, Perth (2013)Google Scholar
  5. 5.
    Song, B.-H., Park, K.-W., Kim, T.Y.: U-health expert system with statistical neural network. Adv. Inform. Sci. Serv. Sci. 3(1), 54–61 (2011)Google Scholar
  6. 6.
    Beulah Devamalar, P.M., Bai, T., Srivatsa, S.K.: An Architecture for a Fully Automated Real-Time Web-Centric Expert System. World Academy of Science, Engineering and Technology (2007)Google Scholar
  7. 7.
    Szajnar, W., Setlak, G.: A concept of building an intelligence system to support diabetes diagnostics. Studia Informatica (2011)Google Scholar
  8. 8.
    Kumar, S., Bhimrao, B.: Development of knowledge Base Expert System for Natural treatment of Diabetes disease. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 3(3) (2012)Google Scholar
  9. 9.
    Bayu, A.T., et al.: An early detection method of type-2 diabetes mellitus in Public Hospital. TELKOMNIKA 9(2), 287–294 (2011)CrossRefGoogle Scholar
  10. 10.
    The Diabetic Exchange List.
  11. 11.
  12. 12.
    Igbal, A., Nagwa, M.: Health Guide for Diabetics. Sudan Federal Ministry of Health (2010)Google Scholar
  13. 13.
    Garcia, M.A., Gandhi, A.J., Singh, T., Duarte, L., Shen, R., Ponder, M.D.S., Ramirez, H.: Esdiabetes (an expert system in diabetes). JCSC 16, 166–175 (2001)Google Scholar
  14. 14.
    Diabetes Education and Prevention World Diabetes Day.
  15. 15.
    Grimm, S., Hitzler, P., Abecker, A.: Knowledge Representation and Ontologies Logic, Ontologies and SemanticWeb Languages, pp. 37–87. University of Karlsruhe, Germany (2007)Google Scholar
  16. 16.
    Al-Ghamdi, A.A.-M., et al.: An expert system of determining diabetes treatment based on cloud computing platforms. Int. J. Comput. Sci. Inform. Technol. 2(5), 1982–1987 (2011)Google Scholar
  17. 17.
    Sue Kirkman, M., Briscoe, V.J.: Diabetes in Older Adults: A Consensus Report, American Diabetes Association and the American Geriatrics Society (2012)Google Scholar
  18. 18.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefzbMATHGoogle Scholar
  19. 19.
    Salem, A-B.M., Roushdy, M., HodHod, R.A.: A rule-base expert system for diagnosis of heart diseases. In: Proceedings of 8th International Conference on Soft Computing MENDEL, Brno, Czech Republic, 5–7 June 2002, pp. 258–263 (2002)Google Scholar
  20. 20.
    Salem, A.-B.M., Voskoglou, M.G.: Applications of CBR methodology to medicine. Egypt. Comput. Sci. J. 37(7), 68–77 (2013). ISSN 1110-2586, Special Issue for EMMIT 9th International Conference for Scientific and Social Development in Mediterranean Countries, Nador, Morocco, 21–23 October 2013Google Scholar
  21. 21.
    Shubrook, J.,,: Standards of Medical Care in Diabetes—2017 Abridged for Primary Care Providers. American Diabetes Association, 15 December 2016Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Information Technology Department, Faculty of Computer and Information TechnologyKarray UniversityKhartoumSudan

Personalised recommendations