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Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.

Theogene Kubahoniyesu^1,2, Ignace Habimana Kabano¹

¹African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda.

|December 5, 2024

Summary

This summary is machine-generated.

Machine learning accurately predicts adverse pregnancy outcomes in Rwanda, identifying high-risk factors like advanced maternal age and multiple unions. This enables timely interventions for better maternal and neonatal health.

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Area of Science:

Public Health
Biostatistics
Machine Learning in Healthcare

Background:

Adverse pregnancy outcomes present significant risks to maternal and neonatal health, leading to morbidity, mortality, and developmental issues.
Predicting these outcomes is crucial for timely intervention and improved healthcare strategies.

Purpose of the Study:

To predict adverse pregnancy outcomes in Rwanda using supervised machine learning algorithms.
To identify key risk factors associated with adverse pregnancy outcomes in the Rwandan population.

Main Methods:

A cross-sectional study using data from 14,634 women in the Rwanda Demographic and Health Survey (2019-2020).
Employed K-fold cross-validation and synthetic minority oversampling technique (SMOTE) for data partitioning and class imbalance.
Evaluated seven machine learning algorithms based on accuracy, precision, recall, F1 score, and AUC.

Main Results:

Prevalence of adverse outcomes: 4.5% miscarriage, 2.1% stillbirth.
Risk factors identified: advanced maternal age (>30 years), multiple unions, and lack of healthcare provider visits.
Protective factors: being married and attending at least two antenatal care visits.
K-nearest neighbors (KNN) model demonstrated superior performance with 86% accuracy, 97% recall, and 0.842 AUC.

Conclusions:

Machine learning, particularly the KNN algorithm, effectively predicts adverse pregnancy outcomes.
These predictive models can facilitate early intervention strategies to improve maternal and neonatal care in Rwanda.