Diabetic Prediction using Classification Method
Vimal Sen1, Krishna Gupta2

1Vimal Sen, M.Tech. Scholar Yagyavalkya Institute of Technology, Jaipur, Rajasthan, India.
2Krishna Gupta, Assistant Professor Yagyavalkya Institute of Technology, Jaipur, Rajasthan, India

Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 264-267 | Volume-9 Issue-2, July 2020. | Retrieval Number: F9718038620/2020©BEIESP | DOI: 10.35940/ijrte.F9718.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Prediction analysis of diabetes mellitus is the main focus of this work. There are mainly three tasks involved in prediction analysis. These tasks are input dataset, feature extraction and classification. The earlier framework makes use of SVM and naïve bayes approaches for predicting this disease. This study implements voting classifier for prediction purpose. It is an ensemble approach. This classifier combines three classification models. These models are SVM, naïve bayes and decision tree. The implementation of available and new technique is carried out in python tool. These approaches give outcomes in terms of different performance parameters. In contrast to other classification models, proposed classification model performs better. 
Keywords: Diabetic, SVM, Naïve Bayes, Feature Extraction.