Open AccessVoting Based Classification Method for Diabetes Prediction
Author(s) -
Harwinder Kaur,
Gurleen Kaur
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1172.0782s619
Subject(s) - naive bayes classifier , voting , computer science , decision tree , support vector machine , artificial intelligence , machine learning , random subspace method , majority rule , random forest , classifier (uml) , data mining , weighted voting , bayes classifier , python (programming language) , pattern recognition (psychology) , politics , political science , law , operating system
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.