Bachelor of Science in Data Science and Analytics
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Item A hybrid machine learning-based type 2 diabetes mellitus risk prediction tool for rural Ugandan settings(Uganda Christian University, 2026-06-11) Lorraine Paula Arinaitwe; Noela Rugogamu; Aloysious SsendiType 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.Item Educonnect: Ai-enabled peer-to-peer learning framework for university students(Uganda Christian University, 2026-06-01) Ronald Tusiime; Hellen Noeline Nambooze; Lynn AmoitEduConnect is a web-based, AI-enabled peer learning platform designed to revolutionize how university students discover study partners, access high quality academic resources, and sustain progress. The project addresses three critical local challenges: fragmented peer-learning coordination, limited personalisation in study support and weak integration between collaboration tools and progress tracking. The implemented client-server architecture utilises a React frontend, Node.js/Express API and SQLite database persistence (sql.js) with optional Python services for advanced NLP intent classification and sequence-to-sequence quiz generation. EduConnect integrates intelligent peer recommendations, multi-discipline resource browsing, adaptive quiz workflows, study groups with direct and group messaging and a chatbot (EduBot) for intent based assistance. A key technical contribution is a trained multinomial logistic regression model for predicting next study topics based on user engagement statistics, trained in Python and exported to a JSON weight matrix for native JavaScript inference. Pilot survey evidence from 50 students indicates strong adoption potential and problem relevance. The average student difficulty in finding study partners was rated at 3.80/5, and overall interest in the platform was 4.36/5, with 78% of respondents willing to join beta testing. Combined with a fully functional, deployed MVP, these findings support the technical viability and educational significance of the EduConnect framework. Keywords: Peer learning, educational technology, recommendation systems, logistic regression, NLP intent classification, student collaboration.