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Gradient Boosting Classifier with Zebra optimization algorithm for pregnancy risk prediction
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DOI：10.1016/j.icte.2026.04.002.png)
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Abstract
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High-risk pregnancy endangers both mother and baby, with one maternal death every two minutes in 2023. This study has proposed three Gradient Boosting models—GB-Base, GB-SMOTE, and ZOA-GB—using the West Lombok Pregnancy Risk Prediction Dataset. GB-Base and GB-SMOTE have achieved 90.46% and 90.28% accuracy, while ZOA-GB, using 10 selected features, has reached 88.89%. GB-SMOTE has shown the best performance with an F-score of 84.41%. SHAP has identified Maternal Age, Hemoglobin, and Parity as key features, and DiCE has validated feature-driven prediction control. The study is limited by a single-source dataset, the absence of external-validation, and unexplored optimizers.
Keywords:
High-risk pregnancy
Maternal death
Gradient Boosting Classifier
SMOTE
Zebra optimization algorithm
SHAP
DiCE
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