Abstract:
Adverse pregnancy outcomes have significant short-term and long-term effects on both
the mother and the infant, these outcomes can lead to neonatal and maternal morbidity
and mortality, with long-term consequences such as developmental delays, chronic
health conditions, and increased healthcare costs.
This study was cross-sectional and utilized the data from Rwanda demographic and
health survey carried on 14634 women (RDHS, 2019-2020). K-Fold cross validation
(k=10) was used to split the dataset, SMOTE solved class imbalance. Descriptive
analysis determined the observed adverse pregnancy outcomes, Multivariate analysis
employed to identify the factors associated with adverse pregnancy outcomes. Seven
ML algorithms were employed and model performance metrics (accuracy, precision,
recall, F1 Score and AUC) were evaluated to identify the best performance algorithms.
The findings revealed that 93.4% of pregnancies resulted in live births, 4.5% ended in
miscarriage or stillbirths (2.1%). The multiple logistic regression analysis indicated that
Advanced maternal age (AOR: 3.452, 95% CI: 1.946-6.680), a higher age at first sexual
intercourse (AOR: 1.421, 95% CI: 1.300 – 1.611), and many unions (AOR: 1.320, 95%
CI: 1.104 – 1.573) were risk factors for adverse pregnancy outcomes. However, the risk
was lower among married women (AOR: 0.894, 95% CI: 0.787 – 0.966) and women
who attended antenatal care (ANC) visits (AOR: 0.801, 95% CI: 0.664 – 0.9615). The
K-nearest neighbors (KNN) model was the most effective model for predicting adverse
pregnancy outcomes with 86% accuracy, precision (89%), recall score (97%), F1 score
(93%) and AUC (0.842).
Predicting adverse pregnancy outcomes is an essential step in ensuring the health and
well-being of both the mother and the infant. It allows for early identification of
potential risks, enabling appropriate interventions and treatments to be implemented to
prevent or manage adverse outcomes..
