Abstract

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. S. Jaya Mala "A Hybrid Apporach of Classification Techniques for Predicting Diabetes using Feature Selection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27991.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/27991/a-hybrid-apporach-of-classification-techniques-for-predicting-diabetes-using-feature-selection/s-jaya-mala

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