A PCA-Enhanced Ensemble Framework for Maternal Health Risk Prediction with SMOTE-ENN Balancing

DOI

10.2991/978-94-6239-664-7_96How to use a DOI?

Keywords

Maternal Health Risk; Ensemble Learning; PCA; SMOTE-ENN; Stacking Ensemble; Machine Learning; Pregnancy Prediction

Abstract

Maternal health complications are a major issue of concern in the world especially in developing countries. Early detection of the high-risk pregnancies is very important in minimizing the morbidity and mortality of the mother. The traditional clinical tests often rely on a few features and manual examination, which can neglect complex nonlinear associative links between the physiological predictors. In this report, an ensemble learning model incorporating Principal Component Analysis (PCA) to reduce the number of dimensions and SMOTE-ENN to balance the classes is proposed to predict the risk of maternal health with high precision. A stacked model with K-Nearest Neighbor (KNN), Random Forest (RF), and XGBoost is used to increase the predictive accuracy. Experimental results on a maternal health dataset indicate that the suggested stacking ensemble is more effective than a single model. It has a precision of 98%, an accuracy of 97.59%, a recall of 98% and an F1-score of 98%. It does well particularly in segmenting the minority; the Mid Risk segment. The methodology provides a reliable and robust framework assist in identifying high-risk pregnancies in the early stages, which will help make timely clinical interventions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tamim Mahmud
AU  - Kulsom Akter Sinthia
AU  - Prosenjit Chandra Biswas
AU  - Md Sanower Hossain
AU  - Sourov Kumar
AU  - Sanzida Afrin Anamika
PY  - 2026
DA  - 2026/06/08
TI  - A PCA-Enhanced Ensemble Framework for Maternal Health Risk Prediction with SMOTE-ENN Balancing
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1422
EP  - 1436
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_96
DO  - 10.2991/978-94-6239-664-7_96
ID  - Mahmud2026
ER  -