A Digital Predictive Healthcare Management System for Sickle Cell Disease Using Machine Learning and Data Visualization Techniques: A Case Study of Uganda
| dc.contributor.author | Tirza Atwiine | |
| dc.date.accessioned | 2025-06-12T10:46:56Z | |
| dc.date.available | 2025-06-12T10:46:56Z | |
| dc.date.issued | 2025-05-06 | |
| dc.description | Undergraduate research | |
| dc.description.abstract | This project presents the design and development of a Digital Predictive Healthcare Management System for Sickle Cell Disease (SCD) using Machine Learning and Data Visualization techniques, with a focus on Uganda. Sickle Cell Disease remains a significant public health challenge in Uganda, with limited access to timely diagnosis, treatment monitoring, and personalized care. The proposed system leverages machine learning algorithms; Random Forest Classifier and LSTM(Long-Short-Term-Memory), to predict potential health risks, analyse and predict future patient data, and support early interventions. Interactive dashboards and visual tools created using React provide healthcare professionals and patients with actionable insights for better disease management. This project aims to enhance decision-making, improve patient outcomes, and support national efforts in digital health transformation, particularly in under-resourced settings. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/2699 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | A Digital Predictive Healthcare Management System for Sickle Cell Disease Using Machine Learning and Data Visualization Techniques: A Case Study of Uganda | |
| dc.type | Thesis |