Prediction of Childbirth Outcomes Using XGBoost, Data-Driven Insights and Evaluation

M. Hariomprakaas, V. Vennila, V. Sharmila, S. Savitha

2025

Abstract

Predicting the childbirth outcome is an important problem in maternal healthcare since it effectively minimizes risks and helps decide an appropriate delivery method. Fueled by these developments in data-science as well as to our knowledge there exist limited predictive models for delivery outcomes, this study presents a solid framework which makes use of machine learning, synthetic data generation and novel preprocessing techniques for prediction of delivery outcomes coded by expert. We addressed the challenge of limited and imbalanced data frequently encountered in medical research by utilizing a robust synthetic data with realistic range of maternal and fetal health parameters. We propose a framework based on the tuned XGBoost classifier, which has both the accuracy and generalizability to meet this challenge, along with a regularized objective function that leads to a solution minimizing predictive performance at the expense of model complexity. Dealing with various data types and missing values, we pre-processed the data using a simple yet powerful pipeline that handles these problems implicitly, and as was previously noted, with the use_SMOTE argument switched to True, deployments are ensured the balancing of classes, as well the sampling of high-risk outputs. We thoroughly assess the model, employ cross-validation and stratified sampling to demonstrate that it is accurate and interpretable. The current study has examined an approach which can scale, but is also transparently operationalized within clinical workstreams, marking progress toward enhanced maternal care outcome.

Paper Citation

in Harvard Style

Hariomprakaas M., Vennila V., Sharmila V. and Savitha S. (2025). Prediction of Childbirth Outcomes Using XGBoost, Data-Driven Insights and Evaluation. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 467-472. DOI: 10.5220/0013931500004919

in Bibtex Style

@conference{icrdicct`2525,
author={M. Hariomprakaas and V. Vennila and V. Sharmila and S. Savitha},
title={Prediction of Childbirth Outcomes Using XGBoost, Data-Driven Insights and Evaluation},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={467-472},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013931500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Prediction of Childbirth Outcomes Using XGBoost, Data-Driven Insights and Evaluation
SN - 978-989-758-777-1
AU - Hariomprakaas M.
AU - Vennila V.
AU - Sharmila V.
AU - Savitha S.
PY - 2025
SP - 467
EP - 472
DO - 10.5220/0013931500004919
PB - SciTePress