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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad Europea (UEM) |
| Repositorio: | ABACUS. Repositorio de Producción Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:abacus.universidadeuropea.com:11268/16926 |
| Acceso en línea: | https://hdl.handle.net/11268/16926 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | 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. |
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