Automatic classification of sarcopenia level in older adults: a case study at Tijuana General Hospital

Vista sencilla de ítem
dc.contributor.authorCastillo Olea, Cristian
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorCarballo Lozano, Christian
dc.contributor.authorZuñiga, Clemente
dc.date.accessioned2026-02-26T12:12:37Z
dc.date.available2026-02-26T12:12:37Z
dc.date.issued2019-09-06
dc.date.updated2026-02-26T12:12:37Z
dc.description.abstractThis paper presents a study based on data analysis of the sarcopenia level in older adults. Sarcopenia is a prevalent pathology in adults of around 50 years of age, whereby the muscle mass decreases by 1 to 2% a year, and muscle strength experiences an annual decrease of 1.5% between 50 and 60 years of age, subsequently increasing by 3% each year. The World Health Organisation estimates that 5-13% of individuals of between 60 and 70 years of age and 11-50% of persons of 80 years of age or over have sarcopenia. This study was conducted with 166 patients and 99 variables. Demographic data was compiled including age, gender, place of residence, schooling, marital status, level of education, income, profession, and financial support from the State of Baja California, and biochemical parameters such as glycemia, cholesterolemia, and triglyceridemia were determined. A total of 166 patients took part in the study, with an average age of 77.24 years. The purpose of the study was to provide an automatic classifier of sarcopenia level in older adults using artificial intelligence in addition to identifying the weight of each variable used in the study. We used machine learning techniques in this work, in which 10 classifiers were employed to assess the variables and determine which would provide the best results, namely, Nearest Neighbors (3), Linear SVM (Support Vector Machines) (C = 0.025), RBF (Radial Basis Function) SVM (gamma = 2, C = 1), Gaussian Process (RBF (1.0)), Decision Tree (max_depth = 3), Random Forest (max_depth=3, n_estimators = 10), MPL (Multilayer Perceptron) (alpha = 1), AdaBoost, Gaussian Naive Bayes, and QDA (Quadratic Discriminant Analysis). Feature selection determined by the mean for the variable ranking suggests that Age, Systolic Arterial Hypertension (HAS), Mini Nutritional Assessment (MNA), Number of chronic diseases (ECNumber), and Sodium are the five most important variables in determining the sarcopenia level, and are thus of great importance prior to establishing any treatment or preventive measure. Analysis of the relationships existing between the presence of the variables and classifiers used in moderate and severe sarcopenia revealed that the sarcopenia level using the RBF SVM classifier with Age, HAS, MNA, ECNumber, and Sodium variables has 82'5 accuracy, a 90'2 F1, and 82'8 precision.en
dc.description.sponsorshipThis research was partially supported by the eVida Group of the Basque Government (grant number IT905-16)en
dc.identifier.citationCastillo-Olea, C., Soto, B. G.-Z., Lozano, C. C., & Zuñiga, C. (2019). Automatic classification of sarcopenia level in older adults: a case study at Tijuana General Hospital. International Journal of Environmental Research and Public Health, 16(18). https://doi.org/10.3390/IJERPH16183275
dc.identifier.doi10.3390/IJERPH16183275
dc.identifier.eissn1660-4601
dc.identifier.issn1661-7827
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5256
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2019 by the authors
dc.subject.otherDiagnosis
dc.subject.otherMachine learning
dc.subject.otherSarcopenia
dc.titleAutomatic classification of sarcopenia level in older adults: a case study at Tijuana General Hospitalen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue18
oaire.citation.titleInternational Journal of Environmental Research and Public Health
oaire.citation.volume16
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
castillo_automatic_2019.pdf
Tamaño:
614.95 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos