Title: Logistic regression model as classifier for early detection of gestational diabetes mellitus

Authors: Priya Shirley Muller; M. Nirmala

Addresses: Department of Mathematics, Sathyabama University, Chennai, India ' Department of Mathematics, Sathyabama University, Chennai, India

Abstract: Gestational diabetes mellitus (GDM) is any degree of glucose intolerance during pregnancy. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity one in the present scenario. In a developing country like India, early detection and prevention will be more cost effective. Oral glucose tolerance test (OGTT) is the crucial method for diagnosing GDM done usually between 24th and 28th week of pregnancy. The proposed work focuses on early detection of GDM without a visit to the hospital for women who are pregnant for the second time onwards (multigravida patients). In recent years, prediction models using multivariate logistic regression analysis have been developed in many areas of healthcare research. With an accuracy of 82.45%, the classifier has proved to be an efficient model for diagnosis of GDM without the conventional method of blood test by providing newly designed parameters as inputs to the model.

Keywords: gestational diabetes mellitus; diagnosis; logistic regression; risk factors.

DOI: 10.1504/IJCAET.2019.098139

International Journal of Computer Aided Engineering and Technology, 2019 Vol.11 No.2, pp.174 - 183

Received: 09 Jan 2017
Accepted: 18 Jan 2017

Published online: 26 Dec 2018 *

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