Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.

Journal: PloS one

PMID: 39637200

Abstract

BACKGROUND: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms.

Authors

Theogene Kubahoniyesu

African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda.
Ignace Habimana Kabano

African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda.

Keywords

Abortion, Spontaneous Adolescent Adult Cross-Sectional Studies Female Humans Live Birth Machine Learning Maternal Age Middle Aged Pregnancy Pregnancy Complications Pregnancy Outcome Risk Factors Rwanda Stillbirth Young Adult

External Resources

View on PubMed Access via DOI PubMed (39637200)

Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.

Abstract

Authors

Keywords

External Resources

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