A Machine Learning Based Web Application for Pre-Eclampsia Risk Prediction, Awareness and Management
| dc.contributor.author | Angela Nina Twine Mukiiza | |
| dc.date.accessioned | 2025-06-12T10:39:47Z | |
| dc.date.available | 2025-06-12T10:39:47Z | |
| dc.date.issued | 2025-05-06 | |
| dc.description | Undergraduate research | |
| dc.description.abstract | Pre-eclampsia is a critical condition affecting pregnant women that is characterized by high blood pressure and potential damage to vital organs. This research focuses on developing a machine learning-based web application designed to predict the risk of pre-eclampsia, enhance awareness and provide management strategies. Utilizing patient data, the application aims to offer accurate predictions and a recommendation. The project involves data collection, model training and application deployment emphasizing the integration of user- friendly interfaces and real-time data processing. The research underscores the importance of early detection and intervention potentially reducing the adverse outcomes associated with pre-eclampsia. By leveraging machine learning algorithms and web technologies, this application aspires to empower healthcare providers and expectant mothers with actionable insights fostering better health outcomes and informed decision-making. This work represents a significant stride towards improving maternal health care through innovative technological solutions. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/2698 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | A Machine Learning Based Web Application for Pre-Eclampsia Risk Prediction, Awareness and Management | |
| dc.type | Thesis |