Journal Title : International Journal of Modern Trends in Engineering and Science
Author’s Name : Dr.S.Sapna
Volume 01 Issue o6 Year 2014
ISSN no: 2348-3121
Page no: 15-18
Abstract— Fuzzy Relational Equation is used to derive medical knowledge from the clinical data which consist of two fuzzy relations on a set of patient and a set of propositions that represent symptoms or diagnosis. In this paper the symptoms are taken along with risk factors to identify the patients suffering from diabetic nephropathy. Fuzzy Systems are being used for solving a wide range of problems in different application domain. Fuzzy Systems allow us to introduce the learning and adaptation capabilities. The fuzzy set framework has been used in several different processes of diagnosis of disease. Fuzzy logic is a computational pattern that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning. Fuzzy relational between the symptoms and risk factors for Diabetic based on the expert’s medical knowledge and also related complications due to some common disorder are considered for prediction. This proposed method is an effort to closely imitate a physician’s insight of symptom-disease relations and his approximate reasoning for decision making.
Keywords— Diabetic, Fuzzy logic, Fuzzy Relational Equation, nephropathy.
 Cornforth .D.J, Jelinek .H.F, Teich .M.C and Lowen .S.B, ‘‘Wrapper Subset Evaluation Facilitates the Automated Detection of Diabetes from Heart Rate Variability Measures”, Proceedings of International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA’04), pp.446-455, (2004).
 Dr.K.Bhujang Shetty, “The New Indian Express, Health Article Tuesday”, pp 1, (2007).
 Novo Nordisk, “Diabetes and You-Your Guide to Living Well with Diabetes”, Novo Nordisk India Private Limited, Lead Group, Bangalore, (2007).
 Dr. A.K. Sethi, “Diabetes Control in your Hands”, Pustak Mahal, Delhi, (2006).
 Radha .R and Rajagopalan .S.P, “Fuzzy Logic Approach for Diagnosis of Diabetes”, Information Technology Journal-Asian Network for Scientific Information, Vol.6, No.1, pp.96-102, (2007).
 Sapna.S, Dr.A.Tamilarasi, “Fuzzy Relational Equation in Preventing Neuropathy Diabetic”, ACEEE-International Journal of Recent Trends in Engineering, Vol-2, Issue-4, ISSN 1797-9617, pp.126-128, (2009).
 Sapna.S, Dr.A.Tamilarasi, “Fuzzy Relational Equation in Evaluating Cardiovascular Diabetic Mellitus”, IJFSRS-International Journal of Fuzzy Systems and Rough Systems, Vol-2, Issue-2, ISSN 0974-858x, pp.79-85, (2009).
 Rama Devi .E and Dr.Nagaveni .N, “Design Methodology of a Fuzzy Knowledgebase System to Predict the Risk of Diabetic Nephropathy”, International Journal of Computer Science Issues, Vol.7, No.5, pp.239-247, (2010).
 Rajeswari .K, Vaithiyanathan .V, Gurumoorthy .T , “Modeling Effective Diagnosis of Risk Complications in Type 2 Diabetes-A Predictive Model for Indian Situation”, European Journal of Scientific Research, Vol.54, No.1, pp.147-158, (2011).
 Sanchez .E, “Medical Diagonsis and Composite Fuzzy Relations”, In: Gupta. M.M., Saridis .G.N., Gaines B.R.(Eds.), Advances in Fuzzy Set Theory and Applications, North Holland, New York, pp.437-444, (1979).
 Czogala .E, Drewniak .J, Pedrycz .W, “Fuzzy Relation Equation on a Finite Set, Fuzzy Sets and Systems”, Vol.7, pp89-101, 1982.
 Lichun .C and Boxing .P, “The Fuzzy Relation Equation with Union or Intersection Preserving Operator, Fuzzy Sets and Systems, Elsevier”, Vol. 25, No. 2, pp.191-204, (1988).
 De Baets .B, “Analytical Solution Methods for Fuzzy Relational Equations”, In: Dubois .D, Prade .H (eds.), Fundamentals of Fuzzy Sets. Handbooks of Fuzzy Sets Series, Vol.1, pp.291-340, Kluwer Academic Publisher, Dordrecht, (2000).
 Louh .L, Wang .W.J and Liaw .Y.K, “New Algorithms for Solving Fuzzy Relation Equations”, Mathematics and Computers in Simulation, Vol.59, pp.329-333 (NSC 89-2213-E008-018), (SCI / EI), (2002).
 Zadeh .L.A, “Fuzzy Sets. Information and Control”, Vol.8, No.3, pp.338-353, (1965).