Evaluation of prevalence of the sarcopenia level using machine learning techniques: case study in Tijuana Baja California, Mexico

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dc.contributor.authorCastillo Olea, Cristian
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorZuñiga, Clemente
dc.date.accessioned2026-03-18T11:29:00Z
dc.date.available2026-03-18T11:29:00Z
dc.date.issued2020-03-15
dc.date.updated2026-03-18T11:29:00Z
dc.description.abstractThe article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.en
dc.description.sponsorshipThis research was partially supported by the eVida Group of the Basque Governmenten
dc.identifier.citationCastillo-Olea, C., Soto, B. G.-Z., & Zuñiga, C. (2020). Evaluation of prevalence of the sarcopenia level using machine learning techniques: case study in Tijuana Baja California, Mexico. International Journal of Environmental Research and Public Health, 17(6). https://doi.org/10.3390/IJERPH17061917
dc.identifier.doi10.3390/IJERPH17061917
dc.identifier.eissn1660-4601
dc.identifier.issn1661-7827
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5510
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2020 by the authors
dc.subject.otherMachine learning
dc.subject.otherPrevalence
dc.subject.otherSarcopenia level
dc.titleEvaluation of prevalence of the sarcopenia level using machine learning techniques: case study in Tijuana Baja California, Mexicoen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue6
oaire.citation.titleInternational Journal of Environmental Research and Public Health
oaire.citation.volume17
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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