Predicting adverse pregnancy outcome in Rwanda using machine learning techniques

Theogene Kubahoniyesu and Ignace Habimana Kabano

PLOS ONE, 2024, vol. 19, issue 12, 1-16

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. Methods: This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019–2020) involving 14,634 women. K-fold cross-validation (k = 10) and synthetic minority oversampling technique (SMOTE) were used to manage dataset partitioning and class imbalance. Descriptive and multivariate analyses were conducted to identify the prevalence and risk factors for adverse pregnancy outcomes. Seven machine learning algorithms were assessed for their accuracy, precision, recall, F1 score, and area under the curve (AUC). Results: Of the pregnancies analyzed, 93.4% resulted in live births, while 4.5% ended in miscarriage, and 2.1% in stillbirth. Advanced maternal age(>30 years),women aged 30–34 years (adjusted odds ratio [AOR] = 5.755; 95% confidence interval [CI] = 3.085–10.074; p

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0312447

DOI: 10.1371/journal.pone.0312447

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