Low Cost Journal,International Peer Reviewed and Refereed Journals,Fast Paper Publication approved journal IJEDR(ISSN 2321-9939) apply for ugc care approved journal, UGC Approved Journal, ugc approved journal, ugc approved list of journal, ugc care journal, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, Low cost research journal, Online international research journal, Peer-reviewed, and Refereed Journals, scholarly journals, impact factor 7.37 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool)
(International Peer Reviewed,Refereed, Indexed, Citation Open Access Journal)
ISSN: 2321-9939 | ESTD Year: 2013

Current Issue

Call For Papers
July 2022

Volume 10 | Issue 3
Last Date : 29 July 2022
Review Results: Within 12-20 Days

For Authors


Indexing Partner

Research Area


Paper Details
Paper Title
Predicting Diabetes Mellitus using Data Mining Techniques
  J. Steffi,  Dr.R.Balasubramanian,  Mr.K.Aravind Kumar

Diabetes is a chronic disease caused due to the expanded level of sugar addiction in the blood. Various automated information systems were outlined utilizing various classifiers for anticipate and diagnose the diabetes. Data mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. Selecting legitimate classifiers clearly expands the correctness and adeptness of the system. Due to its continuously increasing rate, more and more families are unfair by diabetes mellitus. Most diabetics know little about their risk factor they face prior to diagnosis. This paper explores the early prediction of diabetes using data mining techniques. The dataset has taken 768 instances from PIMA Indian Diabetes Dataset to determine the accuracy of the data mining techniques in prediction. Then we developed five predictive models using 9 input variables and one output variable from the Dataset information; we evaluated the five models in terms of their accuracy, precision, sensitivity, specificity and F1 Score measures. The purpose of this study is to compare the performance analysis of Naïve Bayes, Logistic Regression, Artificial neural networks (ANNs), C5.0 Decision Tree and Support Vector Machine (SVM) models for predicting diabetes using common risk factors. The decision tree model (C5.0) had given the best classification accuracy, followed by the logistic regression model, Naïve Bayes, ANN and the SVM gave the lowest accuracy.

Keywords- Data mining, Prediction, Naïve Bayes, Logistic Regression, C5.0 Decision Tree, Artificial Neural Networks (ANN) and Support Vector Machine (SVM).
Publication Details
Unique Identification Number - IJEDR1802080
Page Number(s) - 460-467
Pubished in - Volume 6 | Issue 2 | April 2018
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  J. Steffi,  Dr.R.Balasubramanian,  Mr.K.Aravind Kumar,   "Predicting Diabetes Mellitus using Data Mining Techniques", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 2, pp.460-467, April 2018, Available at :http://www.ijedr.org/papers/IJEDR1802080.pdf
Share This Article

Article Preview

ISSN Details

DOI Details

Providing A digital object identifier by DOI
How to get DOI?

For Reviewer /Referral (RMS)

Important Links

NEWS & Conference

Digital Library

Our Social Link

© Copyright 2022 IJEDR.ORG All rights reserved