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Machine Learning for Perinatal Complication Prediction: A Systematic Review

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Inventive Communication and Computational Technologies (ICICCT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 757))

Abstract

The objective of this systematic review is to analyze the application of machine learning for the prediction of pregnancy complications through an extensive review of published literature. The data sources for this research are scientific journals listed in prominent databases such as PubMed and Scopus. The findings of this research suggest that machine learning has been effectively employed in predicting pregnancy complications in multiple studies. Decision tree, random forest, logistic regression, and neural network are among the various machine learning algorithms that were utilized in this investigation. However, there are limitations to using machine learning technology in predicting pregnancy complications, such as reliance on the quality of data and a lack of transparency in the prediction process. This study provides a comprehensive understanding of the application of machine learning in predicting pregnancy complications and establishes a firm basis for further research in this area.

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Author information

Authors and Affiliations

  1. Faculty of Medicine, Universitas Airlangga, Surabaya, 60132, Indonesia

    Dian Lestari

  2. Computer Science Department, Bina Nusantara University, Jakarta, 11480, Indonesia

    Fairuz Iqbal Maulana

  3. Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta, 1148, Indonesia

    Satria Fadil Persada

  4. Telecommunication Research Center, National Research and Innovation Agency, Jakarta, 11480, Indonesia

    Puput Dani Prasetyo Adi

Authors
  1. Dian Lestari
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  2. Fairuz Iqbal Maulana
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  3. Satria Fadil Persada
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  4. Puput Dani Prasetyo Adi
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Corresponding author

Correspondence to Dian Lestari .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Department of Computer Science (HUMAIN-Lab), International Hellenic University, Thessaloniki, Greece

    George A. Papakostas

  3. Information Systems and Operations Management (ISEG), University of Lisbon, Lisboa, Portugal

    Álvaro Rocha

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Lestari, D., Maulana, F.I., Persada, S.F., Adi, P.D.P. (2023). Machine Learning for Perinatal Complication Prediction: A Systematic Review. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_53

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