Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism

--------------------------------------------------------------------ABSTRACT-----------------------------------------------------------------The fuzzy logic and expert system is an important technique to enhance the machine learning reasoning. In this paper, we propose a fuzzy expert system framework which constructs large scale knowledge based system effectively for diabetes. The knowledge is constructed by using the fuzzification to convert crisp values into fuzzy values. By applying the fuzzy verdict mechanism, diagnosis of diabetes becomes simple for medical practitioners. Fuzzy verdict mechanism uses triangular membership function with mamdani’s inference. Defuzzification method is adopted to convert the fuzzy values into crisp values. The result of the proposed method is compared with earlier method using accuracy as metrics. The proposed fuzzy expert system can work more effectively for diabetes application and also improves the accuracy of fuzzy expert system.

[1]  Novruz Allahverdi,et al.  Design of a hybrid system for the diabetes and heart diseases , 2008, Expert Syst. Appl..

[2]  John H. Lilly,et al.  Evolutionary design of a fuzzy classifier from data , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  J. Buckley,et al.  Fuzzy expert systems and fuzzy reasoning , 2004 .

[4]  Mei-Hui Wang,et al.  A Fuzzy Expert System for Diabetes Decision Support Application , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Novruz Allahverdi,et al.  Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation , 2009, J. Intell. Manuf..

[6]  M.M.B.R. Vellasco,et al.  Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Riccardo Bellazzi,et al.  A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring , 2006, IEEE Transactions on Biomedical Engineering.

[8]  Chang-Shing Lee,et al.  Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition , 2007, Expert Syst. Appl..

[9]  M. Margaliot,et al.  Biomimicry and Fuzzy Modeling: A Match Made in Heaven , 2008, IEEE Computational Intelligence Magazine.

[10]  Kemal Polat,et al.  A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine , 2008, Expert Syst. Appl..

[11]  Lotfi A. Zadeh,et al.  Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008, IEEE Computational Intelligence Magazine.

[12]  David V. Power,et al.  Standards of Medical Care in Diabetes: Response to position statement of the American Diabetes Association , 2006 .

[13]  Kemal Polat,et al.  An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease , 2007, Digit. Signal Process..

[14]  Ricardo Femat,et al.  Fuzzy-Based Controller for Glucose Regulation in Type-1 Diabetic Patients by Subcutaneous Route , 2006, IEEE Transactions on Biomedical Engineering.

[15]  V. Basevi.  Standards of medical care in diabetes--2007. , 2009, Diabetes care.

[16]  Gholam Ali Montazer,et al.  Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation , 2010, Expert Syst. Appl..