Gestational Diabetics Prediction Using Logisitic Regression in R

S. Revathy (), M. Ramesh (), S. Gowri () and B. Bharathi ()
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S. Revathy: Sathyabama Institute of Science and Technology
M. Ramesh: Tata Consultancy Services
S. Gowri: Sathyabama Institute of Science and Technology
B. Bharathi: Sathyabama Institute of Science and Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 739-746 from Springer

Abstract: Abstract Machine learning and data mining methods plays major role in biosciences. Now-a-days data mining methods are used to intelligently transform the information available into valuable knowledge. Gestational Diabetes Mellitus (GDM) is a kind of diabetes that occur in women during pregnancy. Some women develop high blood glucose levels during their gestation. Gestational Diabetes Mellitus if ignored and untreated can result in permanent medical problems to the baby in the future. This is identified as a serious open problem of research which calls for good prediction algorithms to predict the GDM at an earlier stage of gestation. Literature shows a wide range of machine learning algorithms employed for the prediction of GDM. This paper proposes novel prediction framework for gestational diabetes based on Logistic Regression. This results of the framework show promising results of better prediction at an early stage of gestation.

Keywords: Gestational diabetes; Data mining; Machine learning; Logistic regression; Prediction (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_73

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DOI: 10.1007/978-3-030-41862-5_73

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