Voting Based Classification Method for Diabetes Prediction
Harwinder Kaur1, Gurleen Kaur2

Manuscript received on 24 August 2019 | Revised Manuscript received on 05 September 2019 | Manuscript Published on 16 September 2019 | PP: 913-918 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B11720782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1172.0782S619
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Abstract: This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.
Keywords: Voting Based Method, Support Vector Machine, Naïve Bayes, Decision Tree, Diabetes Prediction.
Scope of the Article: Classification