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Discovering Interesting Associations in Gestation Course Data

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

Finding risk factors in pregnancy related to neonatal hypoxia is a challenging task due to the informal nature and a wide scatter of the data. In this work, we propose a methodology for sequential estimation of interestingness of association rules with two sets of criteria. The rules suggest that a strong relationship exists between the specific sets of attributes and the diagnosis. 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.

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Authors and Affiliations

  1. Volodymyr Dahl East Ukrainian National University, Severodonetsk, Ukraine

    Inna Skarga-Bandurova, Tetiana Biloborodova & Maksym Nesterov

Authors
  1. Inna Skarga-Bandurova
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  2. Tetiana Biloborodova
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  3. Maksym Nesterov
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Corresponding author

Correspondence to Inna Skarga-Bandurova .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Skarga-Bandurova, I., Biloborodova, T., Nesterov, M. (2017). Discovering Interesting Associations in Gestation Course Data. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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