Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

Design of an Adaptive Fuzzy Nervous System as a Diagnostic Tool for Diabetes

Document Type : Original Article

Author
Marjan Koochaki Rafsanjani
20.1001.1.27174409.1398.2.2.11.1=DOR
Abstract
Expert Systems Are Widely Used In Medicine To Diagnose Diseases. One Of The Important Factors In Improving The Condition Of Patients Is The Speed Of Action In Diagnosing And Treating The Disease. Diabetes Is One Of The Most Common Diseases In The World And The Incidence Of This Disease Is Increasing Rapidly. Diabetes Is A Metabolic Disorder In The Body. In This Disease, The Speed And Ability Of The Body To Use And Complete Metabolism Of Glucose Decreases And Increases The Amount Of Blood Sugar In The Body. Fuzzy Logic Is An Important Method For Modeling Uncertainty In Expert Systems. In This Article, A Method For Diagnosing Diabetes Using A Fuzzy Expert System Is Presented. Due To The Fact That Membership Functions And Fuzzy Rules Play An Important Role In The Performance Of The Fuzzy Expert System, The Adjustment Of Membership Functions Has Been Done Using The Adaptive Fuzzy Neural Inference System. The Simulation Results Show That This Expert System Based On Fuzzy Rules Reduces The Time Of Diagnosis Of Diabetes And Has An Acceptable Performance With 95% Accuracy Of Diagnosis.
Keywords
Fuzzy Expert System
Neural Network
Adaptive Fuzzy Neural
Diabetes Membership Functions
[1] اسلامی، ا. منطق فازی و کاربردهای آن. دانشگاه شهید باهنر کرمان، چاپ اول، 1391.
 
[2] اسلامی نصرت آبادی، ح.، طاهری، س.م. (۱۳۸۸) طراحی یک سیستم خبره برای تشخیص سرطان خون. ششمین کنفرانس بین المللی مدیریت فناوری اطلاعات و ارتباطات ،تهران. ایران.
 
[۳] فاطمی دخت، ح.، کوچکی رفسنجانی، م. (۱۳۹۴) طراحی یک سیستم خبره فازی براساس الگوریتم بهینه سازی ازدحام ذرات و سیستم عصبی فازی تطبیقی برای تشخیص بیماری دیابت. چهارمین کنگره مشترک سیستمهای فازی و هوشمند ایران، زاهدان، ایران.
 
[4] Abdullah, A. A., Fadil, N.S., Khairunizam, W. (2018) Development of Fuzzy Expert System for Diagnosis of Diabetes. In: Proceedings of 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), Kuching, Malaysia.
 
[5] Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., Rashvand, P. (2018) Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine. 1, 145-172.
 
[6] American diabetes association (2010), (http://www.diabetes.org/diabetes-basics).
 
[7] American diabetes association (2018), (http://www.diabetes.org/diabetes-basics/type-1).
 
[8] American diabetes association (2018),(http://www.diabetes.org/diabetes-basics/type-2).
 
[9] Blake, C.L., Merz, C.J. (1996) UCI repository of machine learning databases. Available: http://www.ics.uci.edu./ mlearn
 
[10] Dande, P., Samant, P. (2018) Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. Tuberculosis, 108, 1–9.
 
[11] Du, K.L., Swamy, M.N.S. (2013) Neural Networks and Statistical Learning, Springer Science & Business Media.
 
[12] Fathi Ganji, M., Saniee Abadeh, M. (2011) A fuzzy classification system based on ant colony optimization for diabetes disease diagnosis. Expert Systems with Applications, 38, 14650-14659.
 
[13] Fausett, L. (1994) Fundamentals of Neural Networks: Architectures, Algorithms and Ap- plications. Prentice-Hall, Inc. Upper Saddle River, NJ, USA.
 
[14] https://fa.wikipedia.org/wiki/
 
[15] Kadhim, M.A. (2018) FNDSB: A fuzzy-neuro decision support system for back pain diagnosis. Cognitive Systems Research, 52, 691-700.
 
[16] Meza-Palacios, R., Aguilar-Lasserre, A.A., Urea-Bogarn, E.L., Vzquez-Rodrguez, C.F., Posada-Gmez, R., Trujillo-Mata, A. (2017) Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2diabetes mellitus. Expert Systems with Applications, 72, 335–343.
 
[17] Muthukaruppan, S., Er, M.J. (2012) A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Systems with Applications, 39, 11657–11665.
 
[18] Raj Kumar, P.A., Selvakumar, S. (2013) Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems. Computer Communications, 36, 303–319.
 
[19] Tack, C. (2018) Artificial intelligence                and machine learning applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice, https://doi.org/10.1016/j.msksp.2018.11.012.
 
[20] Wang, G., Hao, J., Ma, J., Huang, L. (2010) A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Systems with Applications, 37, 6225-6232.
 
[21] Wu, H., Yang, S., Huang, Z., He, J., Wang, X. (2018) Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, 100-107.
 
[22] Zadeh, L.A. (1965) Fuzzy sets. Information and control, 8, 338–353.
 
[23] Zadeh, L.A. (2001) Fuzzy Logic Toolbox for use with MATLAB, MathWorks, 2001.
Fuzzy Systems and its Applications
Volume 2, Issue 2 - Serial Number 4
Open Access Statement
December 2019
Pages 205-222

  • Receive Date 16 April 2020
  • Revise Date 15 July 2020
  • Accept Date 31 August 2020