A hybrid machine learning-based type 2 diabetes mellitus risk prediction tool for rural Ugandan settings
| dc.contributor.author | Lorraine Paula Arinaitwe | |
| dc.contributor.author | Noela Rugogamu | |
| dc.contributor.author | Aloysious Ssendi | |
| dc.date.accessioned | 2026-08-17T12:13:07Z | |
| dc.date.available | 2026-08-17T12:13:07Z | |
| dc.date.issued | 2026-06-11 | |
| dc.description | Undergraduate | |
| dc.description.abstract | Type 2 Diabetes Mellitus (T2DM) is becoming a major public health problem in Uganda, especially in rural communities where access to diagnostic equipment and specialised health-care services is limited. This project presents PreDia, a hybrid machine learning-based risk prediction tool developed to support early screening of T2DM in low-resource rural Ugandan settings. The system uses low-cost and clinically accessible features and combines an XG-Boost predictive model with rule-based clinical guidelines from the World Health Organization (WHO) to improve the reliability of risk assessment. To improve transparency and user trust,SHAP (SHapley Additive exPlanations) was integrated to provide understandable explanations for each prediction. The system also includes an offline-first bilingual interface in English and Luganda to support accessibility among Village Health Teams (VHTs). The predictive model was trained and tested using an augmented version of the Pima Indians Diabetes Dataset adapted to reflect selected physiological trends found in Sub-Saharan African populations. Experimental results showed strong predictive performance, achieving an AUC score of 0.9672, an accuracy of 88.96%, a recall of 87.04%, and a precision score of 82.46%. The findings of this study demonstrate that explainable and offline-capable artificial intelligence tools can support community-level diabetes risk screening in resource-constrained healthcare environments. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/3534 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | A hybrid machine learning-based type 2 diabetes mellitus risk prediction tool for rural Ugandan settings | |
| dc.type | Project Report |