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A Miscarriage Prevention System Using Machine Learning Techniques

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Proceedings of Second Doctoral Symposium on Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1374))

Abstract

Miscarriage or spontaneous abortion is the natural death of the fetus before 20 weeks of pregnancy. Stillbirth is the term used to refer to the fetus's demise after this period. Miscarriage can harm both the parents. One cannot reverse the outcome of pregnancy. The only way to deal with miscarriage is to take certain precautions and prevent it. With this objective, this study uses various machine learning techniques such as Logistic Regression, K-Nearest Neighbors, and Random Forest to predict a pregnancy's outcome based on specific features. This paper focuses on each model's contribution and compares the algorithms’ efficiency based on some standard evaluation measures.

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Authors and Affiliations

  1. Department of Data Science, Christ University, Bangalore, India

    Sarmista Biswas & Samiksha Shukla

Authors
  1. Sarmista Biswas
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  2. Samiksha Shukla
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Corresponding author

Correspondence to Sarmista Biswas .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  2. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  3. Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

    Vineet Kansal

  4. University of Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  5. Department of Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Biswas, S., Shukla, S. (2022). A Miscarriage Prevention System Using Machine Learning Techniques. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_34

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