%0 Journal Article %1 noauthororeditor %A Mala, S. Jaya %D 2019 %J International Journal of Trend in Scientific Research and Development %K Bayes Classification Database Diabetes J48 Miining Mining Naïve SVM %N 5 %P 2506-2510 %R https://doi.org/10.31142/ijtsrd27991 %T A Hybrid Apporach of Classification Techniques for Predicting Diabetes using Feature Selection %U https://www.ijtsrd.com/computer-science/data-miining/27991/a-hybrid-apporach-of-classification-techniques-for-predicting-diabetes-using-feature-selection/s-jaya-mala %V 3 %X 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 Ä 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

@article{noauthororeditor, 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 }, added-at = {2019-09-12T15:23:24.000+0200}, author = {Mala, S. Jaya}, biburl = {https://www.bibsonomy.org/bibtex/28467e68db13e17bfd8d2f58b2bedf1f8/ijtsrd}, doi = {https://doi.org/10.31142/ijtsrd27991}, interhash = {0aeef6b2f9c0444aaa4f989bb124d2c6}, intrahash = {8467e68db13e17bfd8d2f58b2bedf1f8}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {Bayes Classification Database Diabetes J48 Miining Mining Naïve SVM}, language = {English}, month = aug, number = 5, pages = {2506-2510}, timestamp = {2019-09-12T15:23:24.000+0200}, title = {A Hybrid Apporach of Classification Techniques for Predicting Diabetes using Feature Selection }, 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}, volume = 3, year = 2019 }