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dc.contributor.authorGarcía-Vicente, Clara
dc.contributor.authorChushig-Muzo, David
dc.contributor.authorMora-Jiménez, Inmaculada
dc.contributor.authorFabelo, Himar
dc.contributor.authorGram, Inger Torhild
dc.contributor.authorLøchen, Maja-Lisa
dc.contributor.authorGranja, Conceição
dc.contributor.authorSoguero-Ruiz, Cristina
dc.date.accessioned2023-09-01T12:15:05Z
dc.date.available2023-09-01T12:15:05Z
dc.date.issued2023-03-23
dc.description.abstractMachine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Oversampling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). Then, we assessed the impact of combining oversampling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are very close to those provided by real data, maintaining relevant insights, and contributing to increasing the predictive performance. The GAN-based model and a linear classifier outperform other oversampling techniques, improving the area under the curve by 2%. These results demonstrate the capability of synthetic data to help with both determining risk factors and building models for CVD prediction.en_US
dc.identifier.citationGarcía-Vicente, Chushig-Muzo, Mora-Jiménez, Fabelo, Gram, Løchen, Granja, Soguero-Ruiz. Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors. Applied Sciences. 2023;13(7)en_US
dc.identifier.cristinIDFRIDAID 2145735
dc.identifier.doi10.3390/app13074119
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/10037/30624
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalApplied Sciences
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleEvaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)