Diabetes is predicted by classification technique. The data mining tool WEKA has been developed for implementing Support Vector Machine (SVM) classifier. Proposed work is framed with a specific end goal to improve the execution of models. For improving the classification accuracy Support Vector Machine is combined with Feature Selection and percentage Split. Trial results demonstrated a serious change over in the current Support Vector Machine classifier. This approach enhances the classification accuracy and reduces computational time.
Data Mining, Diabetes, Classification, SVM, J48, Naïve Bayes
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