Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification

Voice analysis is a non-invasive tool that can capture subtle motor impairments in Multiple Sclerosis (MS). The objective of this study is to develop and validate a machine learning (ML) framework for the automated classification of MS through acoustic voice analysis. A cohort of 300 gender-balanced...

ver descrição completa

Detalhes bibliográficos
Autores: Delgado Hernández, Jonathan, Betancort Montesinos, Moisés, Romero Arias, Tatiana, Hernández Pérez, Miguel Ángel
Tipo de documento: artigo
Data de publicação:2026
País:España
Recursos:Universidad Europea (UEM)
Repositório:ABACUS. Repositorio de Producción Científica
Idioma:inglês
OAI Identifier:oai:abacus.universidadeuropea.com:11268/16926
Acesso em linha:https://hdl.handle.net/11268/16926
Access Level:Acceso aberto
Palavra-chave:Esclerosis múltiple
Aprendizaje automático
Biomarcadores
Enfermedad del sistema nervioso
Investigación médica
Medicina preventiva
Goal 3: Ensure healthy lives and promote well-being for all at all ages
id ES_b103d9b2eab7701a4750c9fcb6fa8f57
oai_identifier_str oai:abacus.universidadeuropea.com:11268/16926
network_acronym_str ES
network_name_str España
repository_id_str
spelling Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classificationDelgado Hernández, JonathanBetancort Montesinos, MoisésRomero Arias, TatianaHernández Pérez, Miguel ÁngelEsclerosis múltipleAprendizaje automáticoBiomarcadoresEnfermedad del sistema nerviosoInvestigación médicaMedicina preventivaGoal 3: Ensure healthy lives and promote well-being for all at all agesVoice analysis is a non-invasive tool that can capture subtle motor impairments in Multiple Sclerosis (MS). The objective of this study is to develop and validate a machine learning (ML) framework for the automated classification of MS through acoustic voice analysis. A cohort of 300 gender-balanced participants (200 with MS and 100 healthy controls) provided sustained vocal recordings. Fifteen acoustic features were extracted. An elastic network model first identified the most relevant parameters from the development cohort, which were then used to train five supervised ML classifiers.20262026-03-0420262026-03-0120262026-03-01journal articlehttp://purl.org/coar/resource_type/c_6501SMURhttp://purl.org/coar/version/c_71e4c1898caa6e32info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/11268/16926reponame:ABACUS. Repositorio de Producción Científicainstname:Universidad Europea (UEM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:abacus.universidadeuropea.com:11268/169262026-06-11T12:41:27Z
dc.title.none.fl_str_mv Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
title Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
spellingShingle Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
Delgado Hernández, Jonathan
Esclerosis múltiple
Aprendizaje automático
Biomarcadores
Enfermedad del sistema nervioso
Investigación médica
Medicina preventiva
Goal 3: Ensure healthy lives and promote well-being for all at all ages
title_short Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
title_full Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
title_fullStr Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
title_full_unstemmed Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
title_sort Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification
dc.creator.none.fl_str_mv Delgado Hernández, Jonathan
Betancort Montesinos, Moisés
Romero Arias, Tatiana
Hernández Pérez, Miguel Ángel
author Delgado Hernández, Jonathan
author_facet Delgado Hernández, Jonathan
Betancort Montesinos, Moisés
Romero Arias, Tatiana
Hernández Pérez, Miguel Ángel
author_role author
author2 Betancort Montesinos, Moisés
Romero Arias, Tatiana
Hernández Pérez, Miguel Ángel
author2_role author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Esclerosis múltiple
Aprendizaje automático
Biomarcadores
Enfermedad del sistema nervioso
Investigación médica
Medicina preventiva
Goal 3: Ensure healthy lives and promote well-being for all at all ages
topic Esclerosis múltiple
Aprendizaje automático
Biomarcadores
Enfermedad del sistema nervioso
Investigación médica
Medicina preventiva
Goal 3: Ensure healthy lives and promote well-being for all at all ages
description Voice analysis is a non-invasive tool that can capture subtle motor impairments in Multiple Sclerosis (MS). The objective of this study is to develop and validate a machine learning (ML) framework for the automated classification of MS through acoustic voice analysis. A cohort of 300 gender-balanced participants (200 with MS and 100 healthy controls) provided sustained vocal recordings. Fifteen acoustic features were extracted. An elastic network model first identified the most relevant parameters from the development cohort, which were then used to train five supervised ML classifiers.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-03-04
2026
2026-03-01
2026
2026-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
SMUR
http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/11268/16926
url https://hdl.handle.net/11268/16926
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:ABACUS. Repositorio de Producción Científica
instname:Universidad Europea (UEM)
instname_str Universidad Europea (UEM)
reponame_str ABACUS. Repositorio de Producción Científica
collection ABACUS. Repositorio de Producción Científica
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869416874988535808
score 15.198674

Registros relacionados