Accuracy comparison of the data mining classification techniques for the diabetic disease prediction
by Rakesh Garg
International Journal of Healthcare Technology and Management (IJHTM), Vol. 18, No. 3/4, 2021

Abstract: In the present scenario, the speedy use of the data mining (DM) techniques is observed for predicting and categorising symptoms in large medical datasets. Classification is one major DM technique that is widely used for classifying various unnoticed information from various diagnostic data. In a popular country like India, diabetes is characterised as a dangerous disease which has affected the majority of the population. The present research emphasises on the accuracy comparison of the various classifiers such as J48, random forest, sequential minimal optimisation (SMO), stochastic gradient descent (SGD), naive Bayes, logistic regression, random tree, decision stump, simple logistic, Hoeffding tree, Adaboost, and bagging, when applied to diabetic data.

Online publication date: Fri, 26-Nov-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Healthcare Technology and Management (IJHTM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com