Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016.

Journal: BMC women's health
PMID: 38263219

Abstract

BACKGROUND: An unintended pregnancy is a pregnancy that is either unwanted or mistimed, such as when it occurs earlier than desired. It is one of the most important issues the public health system is currently facing, and it comes at a significant cost to society both economically and socially. The burden of an undesired pregnancy still weighs heavily on Ethiopia. The purpose of this study was to assess the effectiveness of machine learning algorithms in predicting unintended pregnancy in Ethiopia and to identify the key predictors.

Authors

  • Daniel Niguse Mamo
    Department of Health Informatics, School of Public Health, Arbaminch University, Arbaminch, Ethiopia. danielniguse1@gmail.com.
  • Yosef Haile Gebremariam
    Department of Public Health, School of Public Health, Arbaminch University, Arbaminch, Ethiopia.
  • Jibril Beshir Adem
    Department of Health Informatics, Institute of Public Health, Arsi University, Assela, Ethiopia.
  • Shimels Derso Kebede
    Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
  • Agmasie Damtew Walle
    Department of Health Informatics, College of Medicine and Health Science, Debre Berhan University, Debre Berhan, Ethiopia.

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