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|
|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. www.ijcttjournal.org. Published by Seventh Sense Research Group.
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.
 UCI Machine Learning Repository- Center for Machine Learning and Intelligent System, http://archive.ics.uci.edu.
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Data Mining, Diabetes, Fuzzy C Means, Support Vector Machine, Sequential Minimal Optimization, Classification.