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INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH
(International Peer Reviewed,Refereed, Indexed, Citation Open Access Journal)
ISSN: 2321-9939 | ESTD Year: 2013

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

Abstract
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
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