Journal of Intelligent Manufacturing

, Volume 20, Issue 2, pp 169–176 | Cite as

Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation

  • Ismail Saritas
  • Ilker A. Ozkan
  • Novruz Allahverdi
  • Mustafa Argindogan


In this study, chronic intestine illness symptoms such as sedimentation and prostate specific antigen are used for the design of fuzzy expert system to determine the drug (salazopyrine) dose. Suitable drug dose for patients is obtained by using data of ten patients. The results of some patients are compared with the doses recommended to them by the physician. As a result, it has been seen that proposed system is helped to shorten the treatment duration and minimize or remove the negative effects of determination of drug dose for helping physicians.


Chronic intestine inflammation Fuzzy expert system Dose of drug Salazopyrine 


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  1. Allahverdi, N. (2002). Expert systems. An artificial intelligence application (248 pp). Istanbul: Atlas.Google Scholar
  2. Allahverdi, N. (2007). Fuzzy logic and systems. Retrieved March 21, 2007 from
  3. Baykal, N., & Beyan, T. (2004). Fuzzy logic expert systems and controller. Ankara: Bıcaklar Kitabevi.Google Scholar
  4. Bellazzi, R., & Siviero, C. (1994). Predictive fuzzy controllers for drug delivery. In Intelligent Systems Engineering, 5–9 September, Hamburg-Harburg, Germany (pp. 262–267).Google Scholar
  5. Gawedal, A. E., Brier, M. E., & Zurada, J. M. (2007). Soft computing methods for drug dosing in renal anemia. Department of Medicine, University of Louisville. Retrieved March 17, 2007 from
  6. Gawedal, A. E., Jacobs, A. A., & Brierl, M. E. (2003). Fuzzy rule-based approach to automatic drug dosing in renal failure. In IEEE International Conference on Fuzzy Systems (pp. 1206–1209).Google Scholar
  7. Kern S.E., Johnson J.O., Westenskow D.R. (1997) Fuzzy logic for model adaptation of a pharmacokinetic-based closed loop delivery system for pancuronium. Artificial Intelligence in Medicine 11(1): 9–31CrossRefGoogle Scholar
  8. Kilic K., Sproule B.A., Türksen I.B., Naranjo C.A. (2004) Pharmacokinetic application of fuzzy structure identification and reasoning. Information Science 162(2): 121–137CrossRefGoogle Scholar
  9. Lada, P., Brier, M. E., & Zurada, J. M. (1999). Therapeutic drug dosing prediction using adaptive models and artificial neural networks. In IEEE International Joint Conference on Neural Networks, Washington, DC, USA (pp. 3699–3673).Google Scholar
  10. Nguyen, H. T., Prasad, N. R., Walker, C. L., & Walker, E. A. (2003). A first course in fuzzy and neural control. New York: Chapman and Hall/CRC.Google Scholar
  11. Ommaty, R. (2007). Vademecum modern drug directory. Istanbul: Pelikan & Tıp Teknik Yayıncılık.Google Scholar
  12. Oshita, S., Nakakimura, K., & Sakabe, T. (1994). Hypertension control during anesthesia. IEEE Engineering in Medicine and Biology, November/December, 667–670.Google Scholar
  13. Per K.K., Lim C.P., Quek S.S., Khoh K.H. (2000) Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor. Pharmaceutical Research 17(11): 1384–1388CrossRefGoogle Scholar
  14. Ross T.J. (1995) Fuzzy logic with engineering applications. McGraw-Hill, New YorkGoogle Scholar
  15. Saritas, I. (2002). Fuzzy control in medicine field. Master Thesis, Selcuk University, Konya.Google Scholar
  16. Wang L.-X. (1997) A course in fuzzy systems and control. Prentice-Hall, New York, p 424Google Scholar
  17. Zadeh L.A. (1965) Fuzzy sets. Information and Control 3: 338–353CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Ismail Saritas
    • 1
  • Ilker A. Ozkan
    • 1
  • Novruz Allahverdi
    • 1
  • Mustafa Argindogan
    • 2
  1. 1.Department of Electronic and Computer EducationSelçuk UniversityKonyaTurkey
  2. 2.Campus Medical Care CenterSelçuk UniversityKonyaTurkey

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