Publication: Identification of predictors of sarcopenia in older adults using machine learning: English Longitudinal Study of Ageing
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Date
2024-11-12
Authors
Pavón Pulido, Nieves ; Domínguez, Ligia ; Blasco García, Jesús Damián ; Veronese, Nicola ; Lucas Ochoa, Ana María ; Fernández Villalba, Emiliano ; González Cuello, Ana María ; Barbagallo, Mario ; Herrero Ezquerro, María Trinidad ; GOING FWD Investigators
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Facultades de la UMU::Facultad de Medicina
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Publisher
MDPI
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DOI
https://doi.org/10.3390/jcm13226794
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info:eu-repo/semantics/article
Description
Abstract
Background: After its introduction in the ICD-10-CM in 2016, sarcopenia is a condition widely considered to be a medical disease with important consequences for the elderly. Considering its high prevalence in older adults and its detrimental effects on health, it is essential to identify its risk factors to inform targeted interventions. Methods: Taking data from wave 2 of the ELSA, using ML-based methods, this study investigates which factors are significantly associated with sarcopenia. The Minimum Redundancy Maximum Relevance algorithm has been used to allow for an optimal set of features that could predict the dependent variable. Such a feature is the input of a ML-based prediction model, trained and validated to predict the risk of developing or not developing a disease. Results: The presented methods are suitable to identify the risk of acquired sarcopenia. Age and other relevant features related with dementia and musculoskeletal conditions agree with previous knowledge about sarcopenia. The present classifier has an excellent performance since the “true positive rate” is 0.81 and the low “false positive rate” is 0.26. Conclusions: There is a high prevalence of sarcopenia in elderly people, with age and the presence of dementia and musculoskeletal conditions being strong predictors. The new proposed approach paves the path to test the prediction of the incidence of sarcopenia in older adults.202
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Citation
Journal of Clinical Medicine. 2024 Nov 12;13(22):6794. doi: 10.3390/jcm13226794. PMID: 39597937; PMCID: PMC11594410.
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