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dc.contributor.author
Ipar, Eugenia
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dc.contributor.author
Cymberknop, Leandro Javier
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dc.contributor.author
Armentano, Ricardo Luis
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dc.date.available
2024-08-20T12:42:50Z
dc.date.issued
2023-09
dc.identifier.citation
Ipar, Eugenia; Cymberknop, Leandro Javier; Armentano, Ricardo Luis; Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights; MDPI; Applied Sciences; 13; 19; 9-2023; 1-22
dc.identifier.issn
2076-3417
dc.identifier.uri
http://hdl.handle.net/11336/242851
dc.description.abstract
With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
MDPI
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
vascular age
dc.subject
machine learning
dc.subject
arterial pressure waveform
dc.subject.classification
Otras Ciencias de la Computación e Información
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-08-19T11:16:48Z
dc.journal.volume
13
dc.journal.number
19
dc.journal.pagination
1-22
dc.journal.pais
Suiza
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dc.description.fil
Fil: Ipar, Eugenia. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Cymberknop, Leandro Javier. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina
dc.description.fil
Fil: Armentano, Ricardo Luis. Universidad de la República; Uruguay
dc.journal.title
Applied Sciences
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/13/19/10585
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/app131910585
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