School of Computing and Informatics Technology (CIT) Collection
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ItemResponsible disease modeling and prediction of Cardiovascular diseases(Makerere University, 2025)The increased number of deaths of cardiovascular diseases among people in both Low- and Middle-Income countries and in developed countries is alarming. There are Machine Learning (ML) models that have been developed for early diagnosis of cardiovascular diseases, however, their success is low due to the black box nature of the models and the trust among the cardiovascular diseases’ health experts is low thus hindering the models’ acceptance. This research study focused on developing explainable AI models to predict the likelihood of acquiring CVDs over a period of ten years from which the best performing one will be chosen. The open cardiovascular disease study dataset was used to develop multiple Machine learning models i.e K-Nearest Neighbors (KNN), Logistic Regression, XGBoost, Catboost, Random Forest, Naive Bayes, Ada Boost, Support Vector Machine, Gradient Boosting Machine, Long Short-Term Memory and Decision tree. The models’ performance was assessed using F1-score, Accuracy, Area Under the Curve, Precision, Recall, sensitivity, specificity and the confusion matrix metrics. From this study, it is observed that the Random Forest model performs better than the other models with an accuracy of 98% followed by XGBoost with 89% and KNN with 88%. Explainable AI techniques (XAI) like SHapley Additive exPlanations (SHAP) explainable technique, Partial Dependence Plots (PDP), Individual Conditional Expectations (ICE) and Local Interpretable Model-agnostic Explanations (LIME), were later applied to all the models to understand how they came to their prediction thus breaking the black box nature of Machine learning models. This research contributes to the identification of cardiovascular diseases risk factors with the use of feature learning and XAI for the early diagnosis of cardiovascular diseases thus aiding in early intervention. The leading risk factors that were established as per the models’ predictions are Age,sex, systolic Blood pressure(SysBP) and Cigarettes per day whereas diabetes, total cholesterol, Blood Pressure Medication (BPMeds) and prevalent Stroke are the least contributing risk factors implying they are not as important in acquiring CVDs.
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ItemExplainable machine learning for viral rebound prediction in patients on antiretroviral therapy(Makerere University, 2025)HIV continues to be a global public health concern, as shown by the approximately 2.1 million HIV-positive patients who were receiving ART without experiencing viral suppression in 2022, according to UNAIDS. Although ART has significantly reduced HIV-related morbidity and mortality, viral rebound (VR) continues to threaten treatment success. In low-resource settings like Uganda, clinical systems have limited data to identify high-risk patients before VR occurs, which increases the likelihood of HIV transmission, treatment failure, drug resistance, and other challenges. Examining VR feature interaction patterns by healthcare workers is challenging due to complex interplay of factors associated with VR. Previous works on VR contributing factors have used less interpretable methods that cannot be trusted and adopted by healthcare workers. Demographic, clinical and laboratory data was preprocessed and divided into chunks of 70%, 15%, and 15% in order to train, validate, and test the six ML models (Hard Voting, Gradient Boosting, LightGBM, Random Forest, XGBoost, and Stacking), respectively. To address class imbalance, SMOTE was applied to the training data, before ML models were trained. Although Hard Voting achieved the highest F1-score (51.56), LightGBM (F1-score of 50.83) was selected as the best-performing ML model due to its superior interpretability when paired with XAI techniques (SHAP, LIME, ELI5, and PDP). Key predictors included past two or more consecutive suppressions, regimen history, adherence patterns, and ART duration. This study demonstrates that ML can provide accurate predictions of VR in ART patients, that can aid in the efficient allocation of healthcare resources by identifying which patients are most at-risk. VR feature interaction patterns will aid in creation of a valuable clinical decision support tool that will notify healthcare workers of patients that need tailored therapies and devise measures to promote viral suppression. XAI techniques integrated ensure that medical professionals understand prediction results and foster proper trust, ultimately strengthening HIV care.
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ItemExplainable machine learning for viral rebound prediction in patients on antiretroviral therapy(Makerere University, 2025)HIV continues to be a global public health concern, as shown by the approximately 2.1 million HIV-positive patients who were receiving ART without experiencing viral suppression in 2022, according to UNAIDS. Although ART has significantly reduced HIV-related morbidity and mortality, viral rebound (VR) continues to threaten treatment success. In low-resource settings like Uganda, clinical systems have limited data to identify high-risk patients before VR occurs, which increases the likelihood of HIV transmission, treatment failure, drug resistance, and other challenges. Examining VR feature interaction patterns by healthcare workers is challenging due to complex interplay of factors associated with VR. Previous works on VR contributing factors have used less interpretable methods that cannot be trusted and adopted by healthcare workers. Demographic, clinical and laboratory data was preprocessed and divided into chunks of 70%, 15%, and 15% in order to train, validate, and test the six ML models (Hard Voting, Gradient Boosting, LightGBM, Random Forest, XGBoost, and Stacking), respectively. To address class imbalance, SMOTE was applied to the training data, before ML models were trained. Although Hard Voting achieved the highest F1-score (51.56), LightGBM (F1-score of 50.83) was selected as the best-performing ML model due to its superior interpretability when paired with XAI techniques (SHAP, LIME, ELI5, and PDP). Key predictors included past two or more consecutive suppressions, regimen history, adherence patterns, and ART duration. This study demonstrates that ML can provide accurate predictions of VR in ART patients, that can aid in the efficient allocation of healthcare resources by identifying which patients are most at-risk. VR feature interaction patterns will aid in creation of a valuable clinical decision support tool that will notify healthcare workers of patients that need tailored therapies and devise measures to promote viral suppression. XAI techniques integrated ensure that medical professionals understand prediction results and foster proper trust, ultimately strengthening HIV care.
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ItemTele-Queue management system for healthcare using machine learning-enhanced triage and reinforcement learning(Makerere University, 2025)Design a machine learning-driven Tele-Queue Management System (TeleQMS) to automate triage, prioritize patients based on symptom criticality and arrival time, and optimize waiting times and resource allocation in government healthcare facilities in Kampala, Uganda.
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ItemContextualisation of syntactic interoperability data standards : a case for health information exchange in Uganda’s healthcare system(Makerere University, 2025)Syntactic interoperability data standards are vital for the seamless exchange and effective utilisation of healthcare information within contemporary health systems. This study focuses on contextualising these standards to advance digital healthcare in Uganda, aligning with the World Health Organisation’s (WHO) strategic framework for 2020-2025. This framework aims to overcome gaps across six building blocks to achieve the Sustainable Development Goal of good health and well-being by enhancing health information systems for improved patient care continuity. Contextualised syntactic interoperability standards are essential for ensuring that patient data is consistently collected, processed, shared, and stored in compatible formats, thereby facilitating interoperability across diverse healthcare environments. Uganda’s healthcare system faces unique challenges that impede effective utilisation of health data, including the absence of standardised data formats, inadequate technical infrastructure, and insufficient data governance policies. Additional barriers include a shortage of skilled personnel, a weak data use culture, limited resources, poor data quality, complacency, limited political will, and inadequate leadership. Existing data interoperability standards, which are predominantly designed for developed countries, often fail to address Uganda’s specific needs due to differing levels of health information management maturity. This study addresses a critical literature gap by presenting a pragmatic approach to contextualising syntactic interoperability data standards specifically for Uganda, contrasting with successful contextualisation in other countries. A systematic three-phase methodology was employed: First, a descriptive cross-sectional survey identified essential Health Information Exchange (HIE) standards using Design Science Research (DSR) methodologies, including brainstorming, systems review, and literature review. Second, standards were developed based on these requirements, covering areas such as patient identification, health information exchange registries, medical imaging management, system digitisation, security, privacy, and capacity building. The contextualized standards were validated by Uganda’s Ministry of Health experts and reviewed by digital health stakeholders using the HIIRETWG tool. Aligned with global frameworks, they aim to enhance data use, improve patient care, foster innovation, and strengthen efficiency and interoperability within Uganda’s healthcare system.