Performance Analysis of Classification Algorithms on Diabetes Dataset
|K. Saravanapriya1 , J. Bagyamani2|
|Correspondence should be addressed to: Dept. of MCA, Sacred Heart College (Autonomous), Tirupattur, India|Dept. of Computer Applications, Chikkanna Government Arts College, Tiruppur, India.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 15-20, Sep-2017
Online published on Sep 30, 2017
Copyright © K. Saravanapriya, J. Bagyamani . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: K. Saravanapriya, J. Bagyamani, “Performance Analysis of Classification Algorithms on Diabetes Dataset”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.15-20, 2017.
MLA Style Citation: K. Saravanapriya, J. Bagyamani "Performance Analysis of Classification Algorithms on Diabetes Dataset." International Journal of Computer Sciences and Engineering 5.9 (2017): 15-20.
APA Style Citation: K. Saravanapriya, J. Bagyamani, (2017). Performance Analysis of Classification Algorithms on Diabetes Dataset. International Journal of Computer Sciences and Engineering, 5(9), 15-20.
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|Healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’ . Today in this hectic lifestyle, one of the major threats to human health is Diabetes Mellitus. Valuable knowledge can be discovered from application of data mining techniques in the Health care System particularly in Diabetes Database. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. This paper aims to analyze the performance of the classification techniques in diabetes data set.|
|Key-Words / Index Term :|
|Diabetes Mellitus, Data Mining, Classification, Naïve Bayes, Random Forest, J48, JRIP, Multilayer Perceptron, KNN, Support Vector Machine, RBF Network, Weka|
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