|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|
 Harleen Kaur and Siri Krishan Wasan, “Empirical Study on Applications of Data Mining Techniques in Healthcare”, Journal of Computer Science, Volume 2, Issue 2, 2006.
 V. Vapnik. The Nature of Statistical Learning Theory. NY: Springer-Verlag. 1995.
 K. Rajesh, V. Sangeetha, “Application of Data Mining Methods and Techniques for Diabetes Diagnosis”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, September 2012.
 K. Saravanapriya, A Study on Free Open Source Data mining Tools”, International Journal of Engineering and Computer Science, Volume 3, Issue 12, 2014.
 Russell, Stuart, Norvig, Peter, “Artificial Intelligence: A Modern Approach (2nd ed.)”. Prentice Hall. 2003 ISBN 978-0137903955
 Ho, Tin Kam, “Random Decision Forests”, Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278–282, 1995
 Wasserman, P.D.; Schwartz, T, “Neural networks. II. What are they and why is everybody so interested in them now?”, pp: 10-15; IEEE Expert, Vol 3, Issue 1, 1988.
 Broomhead, D. S.; Lowe, David, “Radial basis functions, multi-variable functional interpolation and adaptive networks (Technical Report). RSRE 4148, 1988.
 Tejashri N. Giri, S.R. Todamal, “Data mining Approach for Diagnosing Type 2 Diabetes”, International Journal of Science, Engineering and Technology”, Vol 2(8), 2014.
 Dr. M. Renuka Devi, J. Maria Shyla, “Analysis of Various Data Mining Techniques to Predict Diabetes Mellitus”, International Journal of Applied Engineering Research ISSN 0973-4562 Vol 11, Number 1 (2016), pp 727-730.
 Pardha Repalli, “Prediction on Diabetes Using Data mining Approach”, Oklahoma State University.
 R. Sukanya, K. Prabha,“Comparative Analysis for Prediction of Rainfall using Data Mining Techniques with Artificial Neural Network”,Vol 5, Issue 6, pp 288 - 292, June 2017.