Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao’s older adult population through machine learning: a comprehensive analysis approach

Vista sencilla de ítem
dc.contributor.authorVirto, Naiara
dc.contributor.authorDequin, Danielle Marie
dc.contributor.authorRío de Frutos, Xabier
dc.contributor.authorMéndez Zorrilla, Amaia
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2025-02-19T11:22:02Z
dc.date.available2025-02-19T11:22:02Z
dc.date.issued2024-12
dc.date.updated2025-02-19T11:22:02Z
dc.description.abstractBackground Sarcopenia and reduced muscle quality index have garnered special attention due to their prevalence among older individuals and the adverse effects they generate. Early detection of these geriatric pathologies holds significant potential, enabling the implementation of interventions that may slow or reverse their progression, thereby improving the individual’s overall health and quality of life. In this context, artificial intelligence opens up new opportunities to identify the key identifying factors of these pathologies, thus facilitating earlier intervention and personalized treatment approaches. investigate anthropomorphic, functional, and socioeconomic factors associated with muscle quality and sarcopenia using machine learning approaches and identify key determinant factors for their potential future integration into clinical practice. A total of 1253 older adults (89.5% women) with a mean age of 78.13 ± 5.78 voluntarily participated in this descriptive cross-sectional study, which examines determining factors in sarcopenia and MQI using machine learning techniques. Feature selection was completed using a variety of techniques and feature datasets were constructed according to feature selection. Three machine learning classification algorithms classified sarcopenia and MQI in each dataset, and the performance of classification models was compared. Results The predictive models used in this study exhibited AUC scores of 0.7671 for MQI and 0.7649 for sarcopenia, with the most successful algorithms being SVM and MLP. Key factors in predicting both conditions have been shown to be relative power, age, weight, and the 5STS. No single factor is sufficient to predict either condition, and by comprehensively considering all selected features, the study underscores the importance of a holistic approach in understanding and addressing sarcopenia and MQI among older adults. Conclusions Exploring the factors that affect sarcopenia and MQI in older adults, this study highlights that relative power, age, weight, and the 5STS are significant determinants. While considering these clinical markers and using a holistic approach, this can provide crucial information for designing personalized and effective interventions to promote healthy aging.en
dc.description.sponsorshipThis research work has been partially funded by Aristos Campus Mundus (ACM 2019-07). N.V received a University of Deusto grant from the researcher education program (Award number: FPI UD_2022_10)en
dc.identifier.citationVirto, N., Dequin, D. M., Río, X., Méndez-Zorrilla, A., & García-Zapirain, B. (2024). Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao’s older adult population through machine learning: A comprehensive analysis approach. PLoS ONE, 19(12). https://doi.org/10.1371/JOURNAL.PONE.0316174
dc.identifier.doi10.1371/JOURNAL.PONE.0316174
dc.identifier.eissn1932-6203
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2332
dc.language.isoeng
dc.publisherPublic Library of Science
dc.rights© 2024 Virto et al.
dc.titleExploring determinant factors influencing muscle quality and sarcopenia in Bilbao’s older adult population through machine learning: a comprehensive analysis approachen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue12
oaire.citation.titlePLoS ONE
oaire.citation.volume19
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
virto_exploring_2024.pdf
Tamaño:
1.61 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos