Predicting Maternal Mortality Risk: Sub Urban and Rural India

Subbarayappa, Niharika (2024) Predicting Maternal Mortality Risk: Sub Urban and Rural India. Masters thesis, Dublin, National College of Ireland.

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Abstract

Maternal mortality being the critical issue in the global healthcare, especially in developing countries like India, where there is limited access to maternal health care and complications during pregnancy are often fatal. This study addresses these challenges of predicting maternal mortality risk in the rural regions of India by integrating machine learning (ML) and deep learning (DL) models by using data collected through IoT. The study compares the performances of three models: Random Forest, XG Boost and FNN. A hybrid model is built to combine the strengths of these individual models. The dataset includes features such as age, blood sugar levels, temperature of body and heart rates. Data was extensively pre-processed and hyper-parameter tuning was performed to optimize each model. The evaluation of these models is calculated based on accuracy, recall, precision and f1-score. The results showed that the XG Boost model achieved the highest accuracy Manik et al. (2020), slightly outperforming Random Forest model. However, the developed hybrid model which takes the combined outputs of all models has input, demonstrated the improved stability in the predictions. This research shows the potential of machine and deep learning models to predict maternal mortality risk and aiming in reducing the rate and improving maternal health outcomes in resource-limited settings.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kelly, John UNSPECIFIED
Subjects:	H Social Sciences > HQ The family. Marriage. Woman Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Q Science > Life sciences > Medical sciences
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2025 11:22
Last Modified:	26 Aug 2025 11:23
URI:	https://norma.ncirl.ie/id/eprint/8638

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