Open AccessDiabetic Prediction using Classification Method
Author(s) -
Vimal Sen,
K. G. Gupta
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f9718.079220
Subject(s) - naive bayes classifier , computer science , support vector machine , artificial intelligence , decision tree , machine learning , classifier (uml) , quadratic classifier , random forest , pattern recognition (psychology) , bayes classifier , majority rule , cascading classifiers , data mining , random subspace method
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.