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Using Machine Learning to predict and monitor Maternal Health Risk: A Case Study of Materna – an end user Web Application
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Using Machine Learning to predict and monitor Maternal Health Risk: A Case Study of Materna – an end user Web Application

Lare Adeola, Professor Celestine Iwendi and Kater Akeren
Proceedings of the 2025 AI-Driven Smart Healthcare for Society 5.0. ( AdSoc5.0 2025)
IEEE
2025 AI-Driven Smart Healthcare for Society 5.0. ( AdSoc5.0 2025) (Guru Nanak Institute of Technology Kolkata, India, 14/02/2025–15/02/2025)
25/01/2025

Abstract

Maternal Health Risk Machine Learning web applications Machine Learning
Maternal health has become an increasing health problem, especially in the Global South, where the risks, although reducing but not fast enough, continue to register high maternal mortality rates. This research work sought to provide an ergonomic and easily accessible solution by creating a machine learning web application that will be able to detect maternal health with an accuracy of 84%. The web application is a simple form where the user has a set list of questions and answers, based on those choices, a prediction is made. The research hopes this tool will be deployed in areas with high maternal mortality rates in order to bring down the avoidable risks of deaths experienced by pregnant women.
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