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On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets

2025-01-30Research article Open access
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datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg17:Parcerias para a Implementação dos Objetivos
dc.contributor.authorPinheiro, Rafael F.
dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.editorBrunello, Andrea
dc.date.accessioned2025-10-03T10:31:23Z
dc.date.available2025-10-03T10:31:23Z
dc.date.issued2025-01-30
dc.description.abstractFor preventing health complications and reducing the strain on healthcare systems, early identification of diseases is imperative. In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. This article introduces an algorithm that builds upon an earlier methodology to assess biosignals acquired through cardiopulmonary exercise testing (CPET) for identifying metabolic syndrome (MS), heart failure (HF), and healthy individuals (H). Leveraging support vector machine (SVM) technology, a wellknown machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. The model wastrained on CPETdatafrom 45participants, including 15 with MS, 15 with HF, and 15 healthy controls. For binary classification tasks, the SVM with a polynomial kernel and 5-level wavelet transform (SVM-POL-BW5) outperformed similar methods described in the literature. Moreover, one of the main contributions of this study is the development of a multi-class classification algorithm using the SVM employing a linear kernel and 3-level wavelet transforms (SVM-LIN-MW3), reaching an average accuracy of 95%. In conclusion, the application of SVM-based algorithms combined with wavelet transforms to analyze CPET data shows promise in diagnosing various diseases, highlighting their adaptability and broader potential applications in healthcare.eng
dc.description.sponsorshipThis work was funded by Portuguese national funds provided by the Portuguese Foundation for Science and Technology (FCT) (FCT-UIDB/05704/2020) and in the scope of the research project 2 ARTs (PTDC/EMD-EMD/6588/2020). Rafael F. Pinheiro was supported by FCT through the Institutional Scientific Employment Stimulus CEECINST/ 00060/2021. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.identifier.citationPinheiro RF, Fonseca-Pinto R. 2025. On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets. PeerJ Computer Science 11:e2474 https://doi.org/10.7717/peerj-cs.2474
dc.identifier.doi10.7717/peerj-cs.2474
dc.identifier.issn2376-5992
dc.identifier.urihttp://hdl.handle.net/10400.8/14200
dc.language.isoeng
dc.peerreviewedyes
dc.publisherPeerJ
dc.relationCenter for Innovative Care and Health Technology
dc.relation2ARTs -Acessing Autonomic Control in Cardiac Rehabilitation
dc.relation.hasversionhttps://peerj.com/articles/cs-2474/
dc.relation.ispartofPeerJ Computer Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCPET
dc.subjectMulti-class classification
dc.subjectEarly diagnosis systems
dc.subjectHeart disease
dc.subjectMetabolic diseases
dc.titleOn the development of diagnostic support algorithms based on CPET biosignals data via machine learning and waveletseng
dc.typeresearch article
dspace.entity.typePublication
oaire.awardTitleCenter for Innovative Care and Health Technology
oaire.awardTitle2ARTs -Acessing Autonomic Control in Cardiac Rehabilitation
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05704%2F2020/PT
oaire.awardURIhttp://hdl.handle.net/10400.8/14027
oaire.citation.endPage25
oaire.citation.startPage1
oaire.citation.titlePeerJ Computer Science
oaire.citation.volume11
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamConcurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2020
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePinheiro
person.familyNameFonseca-Pinto
person.givenNameRafael
person.givenNameRui
person.identifier.ciencia-id8513-F117-D554
person.identifier.ciencia-id681D-C547-B184
person.identifier.orcid0000-0002-2369-9016
person.identifier.orcid0000-0001-6774-5363
person.identifier.ridK-9449-2014
person.identifier.scopus-author-id57204116615
person.identifier.scopus-author-id26039086400
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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relation.isAuthorOfPublication7eb9d123-1800-4afd-a2f6-91043353011b
relation.isAuthorOfPublication.latestForDiscovery06761113-497b-4db8-9d9e-2577c048fadb
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