Journal of Womens Health, Issues and Care ISSN: 2325-9795

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Research Article, J Womens Health Issues Care Vol: 8 Issue: 1

Analysis of Risk Factors of Gestational Diabetes Mellitus (GDM) Using Data Mining

Prema NS1* and Pushpalatha MP2

1Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India

2Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India

*Corresponding Author : Prema NS
Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India
Tel: 9743912454
E-mail: [email protected]

Received: October 11, 2018 Accepted: June 28, 2019 Published: June 30, 2019

Citation: Prema NS, Pushpalatha MP (2019) Analysis of Risk Factors of Gestational Diabetes Mellitus (GDM) Using Data Mining. J Womens Health, Issues Care 8:1. doi: 10.4172/2325-9795.1000327

Abstract

Diabetes is the common chronic disease and a major health challenge in all population. Gestational diabetes mellitus (GDM) is a type of diabetes developed in women at the time of pregnancy. We present a Data mining (DM) approach to identify the risk factors of Gestational diabetes mellitus (GDM) using different data mining techniques. Dataset used for analysis contains the details of the pregnant women admitted the local hospital of Mysuru, India. The data mining techniques used are k-means clustering, J48 Decision Tree, Random-Forest and Naive-Bayes classifier. Classification accuracy is enhanced by using feature subset selection wrapper approach. Data imbalanced problem is handled by using Synthetic Minority Over-sampling Technique (SMOTE). The performances of the algorithms have been measured and compared in terms of Accuracy.

Keywords: J48Decision tree; Random forest; Naive-Bayes; Gestational Diabetes mellitus; SMOTE; K-means

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