School of Computing and Informatics Technology (CIT) Collection
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ItemResponsible conversational artificial Intelligence for austainable healthcare futures(Makerere University, 2026)The deployment of large language models in healthcare introduces severe risks of miscalibration and spurious feature correlations, where non-expert medical advice precisely mimics clinical authority. To diagnose this representational collapse, we used model-agnostic explainers to audit baseline classification models. This audit revealed a critical performance asymmetry, achieving 83% precision for the authentic advice class but failing on the non-authentic class with only 24% precision. This proved that parametric models rely on lexical mimicry rather than clinical provenance. To structurally correct this reliance on spurious features, we developed a human-in-the-loop machine learning operations pipeline called the prompt engineering and auditing platform. The platform operationalises reinforcement learning from human feedback to generate the clinically audited maternal health dialogue corpus. Then we encoded this corpus with baseline bidirectional encoder representations from transformers. The encoding yielded a deceptively high 79.7% accuracy yet exposed a high expected calibration error and poor out-of-distribution generalisation. To resolve this semantic rigidity, seven retrieval-knowledge enhancement architectures (including RAG, GRAFT, RAFT, GRAG, CAG, and KAG) were benchmarked against metrics, including calibration, bias, fairness, toxicity, accuracy, robustness, and inference time. On evaluation, graph retrieval-augmented fine-tuning was identified as the optimal architecture. This was because it achieved superior calibration and ethical scores by enforcing neuro-symbolic constraints over the transformer's generative space to actively mitigate representational collapse. This work mandates the developed platform as an architecture-agnostic auditing standard for deploying accountable, knowledge-grounded clinical AI, prioritising algorithmic alignment over uncalibrated statistical fluency.
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ItemA portable plant physiological feature image processing technique for groundnuts rosette disease diagnosis(Makerere University, 2025)The monitoring and early detection of groundnut diseases are crucial for effective crop management and disease control. However, existing methods such as DNA-based and serological tests suffer from time-consuming processes and expensive laboratory setups, making them impractical for remote site testing. Additionally, recognizing groundnut rosette disease based on visual characteristics alone is challenging and unreliable. To address these issues, this study aimed to develop a model for Groundnuts Rosette Disease Diagnosis Using Plant Physiological Feature yolov8 Image Processing Technique. The research objectives were three-fold. First, the study aimed to establish the requirements for a groundnut rosette disease detection model using plant physiological feature image recognition technique. Secondly, to design the groundnut rosette disease diagnosis model using plant physiological feature image recognition technique. Third, the performance of the model was evaluated in terms of accuracy, precision, recall, and F1-score, and a comparison was made with existing methods for groundnut rosette disease detection. The study contributed to the development of a mobile application that assists farmers in making quick decisions regarding Groundnut Rosette Disease management. This app leverages the YOLOv8 model to enable farmers to rapidly and accurately identify the disease based on images of their crops. By providing real-time diagnostic capabilities, the application empowers farmers to implement timely interventions, improving overall crop health and yield. Additionally, the app includes resources on best management practices, helping farmers understand how to mitigate the effects of the disease. User-friendly features make it accessible to farmers with varying levels of technical expertise. Ultimately, this tool enhances efficient crop management practices and supports sustainable agriculture by reducing losses associated with Groundnut Rosette Disease. By promoting informed decision-making, the app aims to improve food security and farmers' livelihoods. This study addressed challenges in diagnosing Groundnut Rosette Disease (GRD) by developing a portable YOLOv8 model using plant physiological feature image recognition. The research followed three key objectives: first, identifying critical physiological features— such as changes in leaf morphology, color, and texture—to establish model requirements; second, collecting diverse data across various groundnut growth stages to train the model to recognize disease-related variations; and third, evaluating the model’s performance using accuracy, precision, recall, and F1-score metrics. The model achieved perfect precision (100%) at a confidence threshold of 0.964, significantly surpassing existing methods reporting precision between 0.75 and 0.90. An F1-score of 0.80 at a confidence threshold of 0.454 demonstrated balanced and reliable disease detection. These results are vital for early and accurate diagnosis, enabling timely interventions that reduce yield losses. The portable YOLOv8 model enhances real-time disease diagnosis and empowers farmers to make informed decisions, contributing to better disease management and improved food security in affected regions. This study introduces a novel image processing technique for diagnosing Groundnut Rosette Disease using plant physiological features. The developed model provides a fast, accurate, and reliable tool to assist farmers in managing groundnut crops. The resulting mobile application empowers farmers to make informed decisions, enhancing crop productivity and disease control. Future work will focus on refining the model, incorporating additional features, and integrating with other agricultural technologies.
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ItemInterpretable multi-strategy learning for predictive therapeutic adherence to osteoporosis treatment among chronic patients(Makerere University, 2025)This study presents an interpretable multi-strategy learning framework to predict thera- peutic adherence to osteoporosis treatment among chronic patients, advancing the use of Artificial Intelligence (AI) in adherence prediction. Traditional, ensemble, deep learning, and hybrid models were developed and evaluated using Explainable AI (XAI) techniques LIME, SHAP, and Permutation Feature Importance to identify key adherence factors. The Extreme Gradient Boosting (XGBoost) model achieved the best overall valida- tion performance with 68.3% accuracy, 66.3% F1 score, and 73.4% AUC-ROC and was deployed as a web-based prediction tool on Render for real-time adherence prediction. XAI results showed that lifestyle factors like smoking, low physical activity, and inad- equate calcium intake negatively affected adherence, while regular exercise and sufficient vitamin D intake improved it. These findings highlight an interpretable, data-driven pathway for clinicians and researchers to support personalized interventions and enhance adherence outcomes in osteoporosis management. Deep Learning, Explainable Artifi- cial Intelligence (XAI), Feature Importance, LIME, Machine Learning Interpretability, Osteoporosis, Postmenopausal, SHAP, Therapeutic Adherence, XGBoost
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ItemA framework to improve electronic viral load result distribution to lower facilities : a case study of Kayunga Hospital, Uganda(Makerere University, 2026)This study was conducted to investigate the challenges that lead to long viral load results turnaround time at the lower facilities in Kayunga District. The study objectives were: to determine the requirements for a framework, structured system designed to improve the distribution of electronic viral load results to low level health facilities, and to design that framework to support improved data flow and accessibility, and to evaluate how well the framework, system developed to improve the efficiency and reliability of electronic viral load results distribution, enhance the delivery of these results to the intended facilities. The study used a mixed–methods approach, which combines qualitative and quantitative methods to gain a comprehensive understanding of the issues. Qualitative research helped to explore the experiences and perceptions of healthcare providers and patients. It used methods like interviews to gather in-depth insights into the challenges in electronic viral load result distribution, while Quantitative research provided statistical analysis and trends. It employed surveys and statistical analysis to identify patterns and trends in viral load data. The study used a case study and survey research as a research strategy to conduct an in-depth analysis of a specific healthcare facility to understand the challenges and context of electronic viral load results distribution, questionnaires to collect data from a large sample of healthcare providers and patients, providing a broader understanding of the issue. A sample size of 279 respondents was used from a population of 882 respondents. Data was collected using the questionnaire and interview guide. The response rate of 100% was obtained from which key findings suggested a positive response and provided valuable insights for health facilities to identify critical areas for improvement in the testing processes. From the study, it was learned that to determine the requirements for the framework, we had to first address the identified challenges that lead to long TAT to identify the requirements. It was also learnt that to design the framework, we had to first identify the design choices to fulfil the identified requirements for framework design. Therefore, it was concluded that to overcome the challenges, there is a need to enhance standard operating procedures governing sample handling and processing to address the issue of the longtime taken to process samples and reduce delays in testing processes. This requirement can be achieved if the Ministry of Health (MOH) strengthens primary healthcare delivery systems by introducing data quality policies and practices for the institution, and also ensures that standard operating procedures are integrated into automatic processes at all health facility levels. The study recommendations include the need for continuous improvement activities such as continuing medical education, quality improvement projects, root cause analysis, and monthly staff rotations. For results that are misplaced, developing a root cause analysis, phone call follow-ups, and increasing the frequency of reviewing hub riders from monthly to twice a month.
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ItemPublic participatory GIS tool for supporting citizen involvement in strategic planning of local government service delivery: a case of Kira Municipality in Uganda(Makerere University, 2025)Citizen participation is a cornerstone of effective and democratic local governance. In Uganda, the decentralization framework provides for citizen involvement in planning and decision-making; however, in practice, participation in the strategic planning of Local Government (LG) service delivery remains limited. This study aimed to develop a Public Participatory Geographical Information System (PPGIS) tool for supporting citizen involvement in the strategic planning of LG service delivery. Specifically, the research sought to: (1) examine existing challenges and requirements for citizen participation; (2) design a PPGIS tool to support citizen involvement; (3) implement the tool; and (4) test and validate its effectiveness. An exploratory survey and a case study design were employed, focusing on Kira Municipality. Data was collected through questionnaires, interviews, and document reviews from both citizens and LG officials. Quantitative data was analyzed using descriptive statistics, while qualitative data was thematically analyzed. The findings revealed persistent barriers to citizen participation, including limited GIS capacity, inadequate institutional support, and insufficient access to information. In response, a PPGIS tool (CISP) was designed, implemented, and validated to facilitate interactive and spatially enabled participation in strategic planning. CISP demonstrated the potential to improve transparency, collaboration, and real-time feedback between citizens and LG officials. The study concludes that integrating PPGIS into local governance processes can strengthen participatory decision-making and accountability. It recommends further adoption of such tools across Ugandan LGs to promote inclusive, data-driven, and citizen-centered service delivery.