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Gestational Diabetics Prediction Using Logisitic Regression in R

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • 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)

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.

Suggested Citation

  • S. Revathy & M. Ramesh & S. Gowri & B. Bharathi, 2020. "Gestational Diabetics Prediction Using Logisitic Regression in R," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 739-746, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_73
    DOI: 10.1007/978-3-030-41862-5_73
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