Modelling Cohort Specific Metabolic Syndrome and Cardiovascular Disease Risk using Supervised Machine Learning

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dc.contributor.authorNgcayiya, Paulina Genet
dc.contributor.supervisorRanchod, Pravesh
dc.date.accessioned2024-10-20T20:35:29Z
dc.date.available2024-10-20T20:35:29Z
dc.date.issued2023-08
dc.descriptionA dissertation submitted in fulfilment of the requirements for the degree of Master of Science, to the Faculty of Science, School of Computer Science & Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023.
dc.description.abstractCardiovascular 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.
dc.description.sponsorshipPSG Financial Wealth.
dc.description.submitterMM2024
dc.facultyFaculty of Science
dc.identifier0000-0003-4829-3888
dc.identifier.citationNgcayiya, Paulina Genet. (2023). Modelling Cohort Specific Metabolic Syndrome and Cardiovascular Disease Risk using Supervised Machine Learning. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/41759
dc.identifier.urihttps://hdl.handle.net/10539/41759
dc.language.isoen
dc.publisherUniversity of the Witwatersrand, Johannesburg
dc.rights©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.
dc.rights.holderUniversity of the Witwatersrand, Johannesburg
dc.schoolSchool of Computer Science and Applied Mathematics
dc.subjectCardiovascular Disease
dc.subjectMetabolic Syndrome
dc.subjectRandom Forest
dc.subjectAutoPrognosis
dc.subjectUCTD
dc.subject.otherSDG-3: Good health and well-being
dc.titleModelling Cohort Specific Metabolic Syndrome and Cardiovascular Disease Risk using Supervised Machine Learning
dc.typeDissertation
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