Electronic Theses and Dissertations (Masters)

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Browsing Electronic Theses and Dissertations (Masters) by SDG "SDG-3: Good health and well-being"

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    Federated learning in the detection of Covid -19 in patient Ct-Scans: A practical evaluation of external generalisation
    (University of the Witwatersrand, Johannesburg, 2023-08) Wapenaar, Korstiaan; Ranchod, Pravesh
    This research explores the practical utility of using convolutional neural networks in a federated learning architecture for COVID-19 diagnostics using chest CT-scans, and whether federated learning models can generalise to data from healthcare facilities that did not participate in training. A model that can generalise to these healthcare facilities could provide lower-resourced or over-utilised facilities with access to supplementary diagnostic services. Eleven models are trained using a modified VGG-16. The models are trained using data from five ‘sites’: four sites are single healthcare facilities and the fifth site is a composite of data from a variety of healthcare facilities. Eleven models are trained, evaluated and compared: five ‘independent models’ are each trained with data from a single site; three ‘global models’ are trained using centrally pooled data from a variety of sites; three ‘federated models’ are trained using a federated averaging approach. The site with composite data is held-out and never included in training the federated and global models. With the exception of this composite site, all models achieve a test accuracy of at least 0.93 when evaluated using test data from the sites used in training these models. All models are then evaluated using data from the composite site. The global and federated models achieve a 0.5 to 0.6 accuracy for the composite site, indicating that the model and training regime is unable to achieve useful accuracies for sites non-participant in training. The federated models are therefore not accurate enough to motivate a healthcare facility decision maker to use the federated models as an alternative or supplementary diagnostic tool to radiographers, or to developing their own independent model. Evaluation of the results suggests that high-quality and consistent image pre-processing may be a necessary precondition for the task.
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    Modelling Cohort Specific Metabolic Syndrome and Cardiovascular Disease Risk using Supervised Machine Learning
    (University of the Witwatersrand, Johannesburg, 2023-08) Ngcayiya, Paulina Genet; Ranchod, Pravesh
    Cardiovascular Disease (CVD) is the leading cause of death worldwide, with Coronary Heart Disease (CHD) being the most common type of CVD. The consequences of the presence of CVD risk factors often manifest as Metabolic Syndrome (MetS). In this study, a dataset from the Framingham Heart Study (FHS) was used to develop two different kinds of CHD risk prediction models. These models were developed using Random Forests (RF) and AutoPrognosis. Performance of the Framingham Risk Score model (AUC-ROC: 0.633) on the FHS dataset was used as the benchmark. The RF model with optimized hyperparameters (AUC-ROC: 0.728) produced the best results. This was by a very small margin to the AutoPrognosis model with an ensemble pipeline (AUC-ROC: 0.714). The performance of RF against AutoPrognosis when predicting the existence of MetS was evaluated using a dataset from the National Health and Nutrition Examination Survey (NHANES). The RF model with optimized hyperparameters (AUC ROC: 0.851) produced the best results. This was by a small margin to the AutoPrognosis model with an ensemble pipeline (AUC-ROC: 0.851). Datasets, varying in size from 100 to 4900, were used to test the performance of RF against AutoPrognosis. The RF model with optimized hyperparameters had the best performance results.