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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 Verma 2
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.