Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine

International Journal of Computer Trends and Technology (IJCTT)          
© 2014 by IJCTT Journal
Volume-11 Number-2
Year of Publication : 2014
Authors : Ravi Sanakal , Smt. T Jayakumari
DOI :  10.14445/22312803/IJCTT-V11P120


Ravi Sanakal , Smt. T Jayakumari."Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine". International Journal of Computer Trends and Technology (IJCTT) V11(2):94-98, May 2014. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Clinical decision-making needs available information to be the guidance for physicians. Nowadays, data mining method is applied in medical research in order to analyze large volume of medical data. This study attempts to use data mining method to analyze the databank of Diabetes disease and diagnose the Diabetes disease. This study involves the implementation of FCM and SVM and testing it on a set of medical data related to diabetes diagnosis problem. The medical data is taken from UCI repository, consists of 9 input attributes related to clinical diagnosis of diabetes, and one output attribute which indicates whether the patient is diagnosed with the diabetes or not. The whole data set consists of 768 cases.

[1] UCI Machine Learning Repository- Center for Machine Learning and Intelligent System,
[2]Pardha Repalli, “Prediction on Diabetes Using Data mining Approach”.
[3] Joseph L. Breault., “Data Mining Diabetic Databases:Are Rough Sets a Useful Addition”.
[4] G. Parthiban, A. Rajesh, S.K.Srivatsa, “Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method “, International Journal of Computer Applications (0975 – 8887) Volume 24– No.3, June 2011.
[5] P. Padmaja, “Characteristic evaluation of diabetes data using clustering techniques”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.11, November 2008.
[6] Lin, C., Lee, C., “Neural Fuzzy Systems,” PrenticeHall, NJ, 1996.
[7] Tsoukalas, L., Uhrig, R., “Fuzzy and Neural Approaches in Engineering,” John Wiley & Sons, Inc., NY, 1997.
[8] De Oliveira, J. V., & Pedrycz, W. (2007). Advances in fuzzy clustering and its applications. (1 ed., pp. 4-69). London: Wiley.
[9] Everitt, S., Landau, S., Leese, M. (2011). Cluster Analysis. (5 ed., pp. 76-80). London: Wiley.
[10] T. J. Ross, Fuzzy Logic with Engineering Applications, Third Edition, ISBN: 978-0-470-74376-8, John Wiley & Sons, 2010
[12].Yamaguchi M, Kaseda C, Yamazaki K, Kobayashi M. “Prediction of blood glucose level of type 1 diabetics using response surface methodology and data mining”. Med Biol Eng Comput.2006; 44(6):451–7.
[13] Centers for Disease Control and Prevention. National diabetes factsheet: national estimates and general information on Diabetes and prediabetes in the United States, 2011. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2011.
[14] Vapnik, V., Estimation of Dependences Based on Empirical Data, Springer-Verlag, (1982).
[15] Cortes, C., Vapnik, V., "Support Vector Networks," Machine Learning, 20:273-297, (1995).
[16] Burges, C. J. C., "A Tutorial on Support Vector Machines for Pattern Recognition," submitted to Data Mining and Knowledge Discovery,, (1998).

Data Mining, Diabetes, Fuzzy C Means, Support Vector Machine, Sequential Minimal Optimization, Classification.