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

Oladimeji, Temitope Olumide (2024) Understanding Risks for Maternal Mortality in Rural Bangladesh Using XGBoost, Random Forest, and Decision Tree ML Models. Masters thesis, Dublin, National College of Ireland.

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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 post-hyperparameter 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, offering significant implications for early and accurate prediction of pregnancy-related risks.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Simiscuka, Anderson UNSPECIFIED
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 > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	25 Aug 2025 08:34
Last Modified:	25 Aug 2025 08:34
URI:	https://norma.ncirl.ie/id/eprint/8601

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