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http://hdl.handle.net/10662/13358
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Campo DC | Valor | idioma |
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dc.contributor.author | Naranjo, Lizbeth | - |
dc.contributor.author | Pérez Sánchez, Carlos Javier | - |
dc.contributor.author | Campos Roca, Yolanda | - |
dc.contributor.author | Madruga Escalona, Mario | - |
dc.date.accessioned | 2022-01-13T12:21:16Z | - |
dc.date.available | 2022-01-13T12:21:16Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://hdl.handle.net/10662/13358 | - |
dc.description.abstract | Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system. | es_ES |
dc.description.sponsorship | This research has been funded by Agencia Estatal de Investigación, Spain (Project MTM2017-86875-C3-2-R), Junta de Extremadura, Spain (Projects IB16054, GR18108 and GR18055), and the European Union (European Regional Development Funds). Lizbeth Naranjo has also been partially funded by UNAM-DGAPA-PAPIIT (Project IN118720), Mexico. Mario Madruga has been funded by Ministerio de Universidades under the doctoral fellowship FPU18/03274. | es_ES |
dc.format.extent | 10 p. | es_ES |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Reinke's edema | es_ES |
dc.subject | Variable selection | es_ES |
dc.subject | Replicated measurements | es_ES |
dc.subject | Regularization | es_ES |
dc.subject | Acoustic features | es_ES |
dc.subject | Classification | es_ES |
dc.subject | Características acústicas | es_ES |
dc.subject | Clasificación | es_ES |
dc.subject | Edema de Reinke | es_ES |
dc.subject | Regularización | es_ES |
dc.subject | Mediciones replicadas | es_ES |
dc.subject | Selección de variables | es_ES |
dc.title | Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings | es_ES |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | es_ES |
dc.subject.unesco | 3311.01 Tecnología de la Automatización | es_ES |
dc.subject.unesco | 2411.14 Fisiología del Lenguaje | es_ES |
dc.subject.unesco | 1203.20 Sistemas de Control Medico | es_ES |
dc.subject.unesco | 3314 Tecnología Médica | es_ES |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | Naranjo, E., Pérez Sánchez, C.J., Campos Roca, Y., Madruga Escalona, M. (2021). Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings. Artificial Intelligence in Medicine, 120, 102162. https://doi.org/10.1016/j.artmed.2021.102162 | es_ES |
dc.type.version | publishedVersion | es_ES |
dc.contributor.affiliation | Universidad Nacional Autónoma de México | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Matemáticas | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Tecnología de los Computadores y de las Comunicaciones | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S093336572100155X?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.artmed.2021.102162 | - |
dc.identifier.publicationtitle | Artificial Intelligence in Medicine | es_ES |
dc.identifier.publicationfirstpage | 102162-1 | es_ES |
dc.identifier.publicationlastpage | 102162-10 | es_ES |
dc.identifier.publicationvolume | 120 | es_ES |
dc.identifier.e-issn | 0933-3657 | - |
Colección: | DMATE - Artículos DTCYC - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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j.artmed.2021.102162.pdf | 853,77 kB | Adobe PDF | Descargar |
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