A Fuzzy Rule based Expert System for T2DM Diagnosis

  • Authors

    • A. D.Dhivya
    • A. Felix
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.21034
  • Linguistic term, Fuzzy number, Fuzzy Expert System, Diabetes.

  • Abstract

    Expert system is an intelligent system to captures the knowledge of a human expert in a specific area. It is capable to make decisions and dealing with ambiguous data. It is used to take an expert view in the absence of a human expertise. Moreover, it is not possible that everyone is expert in every field; to overcome this kind of situation, the expert system is called to handle the complex cases [7]. Due to non availability of the doctor, sometimes patient's life is in risk and lead to death due to not diagnose the disease properly as there are several diseases whose symptoms are quite similar in initial stage. Hence, the objective of this paper is to design the expert system for diagnosing the diabetes to go for early treatment.

     

     

  • References

    1. [1] Ali Adeli., & Mehdi Neshat. (2010), A Fuzzy Expert System for Heart Disease Diagnosis, Proceedings for the International Multi-Conference of Engineers and Computer Scientists, Vol-I, 136-139.

      [2] Hasan Mir Anamul. Khaja. Sher-E-Alam., & Ahsan Raja Chowdhury. (2010), Human disease diagnosis using a fuzzy expert system, Journal of Computing, 2(6), 66-70.

      [3] Ismail Saritas., Novruz Allahverdi., & Ibrahim Unal Sert. (2003), A Fuzzy Expert System Design for Diagnosis of Prostate Cancer, International Conference on Computer Systems and Technologies - Compsystech.

      [4] Kalpana. M., Senthil Kumar. A.V. (2012), Design And Implementation of Fuzzy Expert System Using Fuzzy Assessment Methodology, International Journal of Science And Applied Information Technology, 1(1), 39-45.

      [5] Kandel, A. (1992), Fuzzy Expert Systems. CRC Press, Inc., Boca Raton.

      [6] Maryam Mirsharif., Mahmoud Alborzi., & Amir Ashkan Nasir Pour. (2014), IA Fuzzy Expert System & Neuro-Fuzzy System Using Soft Computing For Gestational Diabetes Mellitus Diagnosis, Inte. J. Of Information, Security snf System Magmt., 3(1), 249-252.

      [7] Najar Yousra., Ketata Raouf., & Ksouri Mekki. (2013), A Study On The Conception Of Generic Fuzzy Expert System For Surveillance, Int. J. Of Advanced Comp. Sci. Appli., 4(8), 218-227.

      [8] Nonso Nnamoko et.al. (2013), Fuzzy Expert System For Type 2 Diabetes Mellitus (T2DM) Management Using Dual Inference Mechanism, Data Driven Wellness: Association For The Advancement Of Artificial Intelligence, 67-70.

      [9] Rama Devi. E., Nagaveni. N. (2010), Design Methodology of a Fuzzy Knowledgebase System to Predict the Risk Of Diabetic Nephropathy. International Journal Of Comp. Sci. Issues, 7(5), 239-247.

      [10] Siler W., Buckley J. (2005), Fuzzy Expert Systems and Fuzzy Reasoning, John Wiley & Sons, Inc., New Jersey.

      [11] Tawfik Saeed Zeki., et.al, (2012), An Expert System For Diabetes Diagnosis, American Academic & Scholarly Research Journal, 4(5), 1-13.

      [12] Zadeh, L.A. (1994), Soft Computing and Fuzzy Logic, IEEE software,11(6), 48-56.

      [13] Zadeh, L.A., (1965), Fuzzy sets, Information and Control, 8 (1965), 338-353.

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  • How to Cite

    D.Dhivya, A., & Felix, A. (2018). A Fuzzy Rule based Expert System for T2DM Diagnosis. International Journal of Engineering & Technology, 7(4.10), 432-435. https://doi.org/10.14419/ijet.v7i4.10.21034

    Received date: 2018-10-05

    Accepted date: 2018-10-05

    Published date: 2018-10-02