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OJBTM
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
ABSTRACT
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.
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