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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, Nave Bayes.


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