Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning

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dc.contributor.authorTorres, Romina
dc.contributor.authorZurita, Christopher
dc.contributor.authorMellado, Diego
dc.contributor.authorNicolis, Orietta
dc.contributor.authorSaavedra, Carolina
dc.contributor.authorTuesta, Marcelo
dc.contributor.authorSalinas, Matías
dc.contributor.authorBertini, Ayleen
dc.contributor.authorPedemonte, Oneglio
dc.contributor.authorQuerales, Marvin
dc.contributor.authorSalas, Rodrigo
dc.date.accessioned2024-09-05T19:06:05Z
dc.date.available2024-09-05T19:06:05Z
dc.date.issued2023-02
dc.descriptionIndexación: Scopus.
dc.description.abstractCardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of (Formula presented.) and an (Formula presented.) of (Formula presented.), where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an (Formula presented.) up to (Formula presented.). The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase. © 2023 by the authors.
dc.description.urihttps://www.mdpi.com/2075-4418/13/3/508
dc.identifier.citationDiagnostics. Volume 13, Issue 3. February 2023. Article number 508
dc.identifier.doi10.3390/diagnostics13030508
dc.identifier.issn2075-4418
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/59870
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights.licenseCC BY 4.0 Attribution 4.0 International Deed
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCardiovascular Rehabilitation
dc.subjectJoint Distribution Adaptation
dc.subjectMachine Learning
dc.subjectTransfer Feature Learning
dc.titlePredicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning
dc.typeArtículo
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