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Extracting Interesting Rules from Gestation Course Data for Early Diagnosis of Neonatal Hypoxia

  • Inna Skarga-BandurovaEmail author
  • Tetiana Biloborodova
  • Maksym Nesterov
Systems-Level Quality Improvement
  • 39 Downloads
Part of the following topical collections:
  1. Artificial Intelligence Application in Health Informatics

Abstract

The topic of neonatal hypoxia is of paramount importance to anyone who cares during pregnancy and childbirth. Modern medicine associates this pathology with severe problems in the prenatal period. Underlying diseases of the mother during pregnancy, her anamnesis of life are the leading causes of complications in the newborn. Nevertheless, patterns of fetal hypoxia and neonatal hypoxia, as well as mechanisms of hypoxic-ischemic encephalopathy in newborns, remains poorly known and require further research. This study is focused on finding risk factors related to the chronic fetal hypoxia and defining a group of signs for diagnosing neonatal hypoxia. The real data of 186 pregnant women at the gestation age from 12 to 38 weeks were analyzed. A methodology for discovering interesting associations in gestation course data is proposed. Technique for association rules mining and rules selection by the neonatal hypoxia under study is discussed. The rules suggest that a strong relationship exists between the specific sets of attributes and the diagnosis. As a result, we set up a profile of the pregnant woman with a high likelihood of hypoxia of the newborn that would be beneficial to medical professionals.

Keywords

Neonatal hypoxia Gestation course data Mining association rules Measure of interestingness 

Notes

Compliance with Ethical Standards

Conflict of interest

None of the authors has any conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Volodymyr Dahl East Ukrainian National UniversitySeverodonetskUkraine

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