Understanding Risks for Maternal Mortality in Rural Bangladesh Using XGBoost, Random Forest, and Decision Tree ML Models

Oladimeji, Temitope, Muntean, Cristina Hava and Simiscuka, Anderson Augusto (2024) Understanding Risks for Maternal Mortality in Rural Bangladesh Using XGBoost, Random Forest, and Decision Tree ML Models. In: The 2024 International Conference on Computational Science and Computational Intelligence (CSCI'24). American Council on Science and Education, Las Vegas, USA.

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Abstract

This paper explores the application of machine learning models for predicting pregnancy risks, focusing on the performance comparison of XGBoost, Random Forest, and Decision Tree classifiers. The motivation behind this research stems from the critical need for early identification of high-risk pregnancies to improve maternal health outcomes. Using a dataset consisting of anonymous information from pregnant women in rural Bangladesh, this study implements feature scaling, standardization, and encoding to prepare the data. Both pre- and posthyperparameter tuning results are analysed, with additional focus on handling imbalanced data through the application of SMOTE (Synthetic Minority Oversampling Technique). The evaluation metrics include accuracy, precision, recall, F1-score, and ROC curves for each class. Key findings indicate that XGBoost outperforms the other models, particularly after hyperparameter tuning and SMOTE application, achieving an accuracy of 82%. The study emphasizes the importance of advanced machine learning techniques in healthcare, o↵ering significant implications for early and accurate prediction of pregnancy-related risks.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RG Gynecology and obstetrics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	07 Jan 2025 16:46
Last Modified:	07 Jan 2025 16:46
URI:	https://norma.ncirl.ie/id/eprint/7278

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