Vaishali Jain; Supriya Raheja

Improving the Prediction Rate of Diabetes using Fuzzy Expert System

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Author(s)

Vaishali Jain ^1,* Supriya Raheja ¹

1. Department of CSE & IT, ITM University, Gurgaon-122017, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.10.10

Received: 23 Jan. 2015 / Revised: 12 May 2015 / Accepted: 10 Jul. 2015 / Published: 8 Sep. 2015

Index Terms

Fuzzy Logic, Fuzzy Verdict Mechanism, Expert System, Fuzzy Logic based Diabetes Diagnosis System (FLDDS)

Abstract

The use of fuzzy logic in disease diagnosis is very common and beneficial as it incorporates the knowledge and experience of physician into fuzzy sets and rules. Most of the research proposed different systems for the diabetes diagnosis. But their accuracy of prediction is not accurate. So, the proposed system presents promising approach for accurately predicting the diabetes by considering the different parameters which are helpful in the diagnosis of diabetes. The proposed fuzzy verdict mechanism takes the information collected from the patients as inputs in the form of datasets. System considers both rules and physicians knowledge to provide the prediction rate of diabetes. Evaluation shows the approach results in better accuracy as compared to other prediction approaches.

Cite This Paper

Vaishali Jain, Supriya Raheja, "Improving the Prediction Rate of Diabetes using Fuzzy Expert System", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.10, pp.84-91, 2015. DOI:10.5815/ijitcs.2015.10.10

Reference

[1]M.Kalpana and Dr. A.V Senthil Kumar, “Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism”, International Journal Advanced Networking and Applications Volume: 03, Issue: 02, pp 1128-1134, 2011.

[2]Nicole Sprunk, Alejandro Mendoza Garcia, Robert Bauernschmitt and Alois Knoll, “Evaluation of an adaptive algorithm for fuzzy type-2 control in blood pressure regulation”, IEEE International Conference on Fuzzy Systems, Hyderabad, India, July 07-10, 2013, Proceedings., pp 1-5, 2013.

[3]Ashish Patel, Shailendra K Gupta, Qamar Rehman and M. K. Verma, “Application of Fuzzy Logic in Biomedical Informatics”, Journal of Emerging Trends in Computing and Information Sciences, pp 57-62, 2013.

[4]Chang-Shing Lee, “A Fuzzy Expert System for Diabetes Decision Support Application”. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 41, no. 1, Feb. 2011.

[5]Manoranjan Kumar Singh, L. Rakesh and Aniket Ranjan (2010),“Evaluation of the Risk of Drug Addiction with the Help of Fuzzy Sets”, Journal of Applied Computer Science & Mathematics, no. 9 (4) /2010, Suceava, pp 98-103, 2010.

[6]Songhua Xie, Dehua Li and Hui Nie, “Research on the Selection of Innovation Compound Using Possibility Construction Space Theory and Fuzzy Clustering”, 2009 Second International Conference on Intelligent Computation Technology and Automation, pp 788-791, 2009.

[7]Angela Torres and Juan J. Nieto, “Fuzzy Logic in Medicine and Bioinformatics”, Journal of Biomedicine and Bioinformatics, 2006.

[8]L. B. Goncalves, M. M. B. R. Vellasco, M. A. C.Pacheco, and F. J. de Souza, “Inverted hierarchical neuro-fuzzy BSP system: A novel neuro-fuzzy model for pattern classification and rule extraction in databases”, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Application and Reviews, vol. 36, no. 2, pp 236-248, March 2006.

[9]Klaus-Peter Adlassnig, “Fuzzy Systems in Medicine” Conference Proceedings of the 2nd International Conference in Fuzzy Logic and Technology, Leicester, United Kingdom, pp 2-5, September 5-7, 2001.

[10]J. Demouy, J. Chamberlain, M. Harris, and L. H. Marchand, “The Pima Indians: Pathfinders of Health”. Bethesda, MD: Nat. Inst. Diabetes Digestive Kidney Diseases, 1995.

[11]http://en.wikipedia.org/wiki/Fuzzy_logic.

[12]http://in.mathworks.com/help/fuzzy/types-of-fuzzy-inference-systems.html.

[13]M. Margaliot, “Bio mimicry and fuzzy modelling: A match made in heaven”, IEEE Computational Intelligence Magazine, vol. 3, no. 3, pp. 38-48, August 2008.

[14]L. A. Zadeh, “Toward human level machine intelligence. Is it achievable? The need for a paradigm shift”, IEEE Computational Intelligence Magazine, vol. 3, no. 3, pp. 11-22, Aug. 2008.

[15]http://in.mathworks.com/products/matlab/.

[16]C. S. Lee and M. H. Wang, “Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition”, Expert System Application, vol. 33, no. 3, pp. 606-619, Oct. 2007.

International Journal of Information Technology and Computer Science (IJITCS)