©1996-2019. All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking: To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.
Online Journal of Bioinformatics ©
Volume 15 (2): 210-217, 2014.
Classification of Type-2 Diabetes Microarray Data by Support Vector Machine and Naive Bayes Classifier.
Rahul Mekala1, Chandan Kumar Verma2.
1Department of Mathematics & Computer Applications, 2Department of Mathematics & Computer Applications, MANIT, Bhopal, India
Mekala R, Verma CH., Classification of Type-2 Diabetes Microarray Data by Support Vector Machine and Naive Bayes Classifier, Onl J Bioinform., 15 (2): 210-217, 2014. Type-2 Diabetes is a serious health issue and the design of a classifier for its detection could be useful. The Pima Indian Diabetic Database for the UCI machine learning laboratory has been used for testing data mining algorithms for prediction accuracy of Type-2 Diabetes data classification. The method presented here uses Support Vector Machine (SVM) and Naive Bayes with machine learning as classifiers for diagnosis of Type-2 Diabetes. The Machine Learning Method focuses on classifying Type-2 Diabetes disease from a high dimensional microarray dataset. Results suggest that SVM could be used for diagnosing Type-2 Diabetes disease but its performance could be improved by feature subset selection process.
Key-Words: Diabetes Type 2, Classifiers, Support Vector Machine, Naïve Bayes.