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10.3390/jcm13226794
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Article

Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing

by
Nieves Pavón-Pulido
2,
Ligia Dominguez
3,4,
Jesús Damián Blasco-García
1,
Nicola Veronese
3,
Ana-María Lucas-Ochoa
1,
Emiliano Fernández-Villalba
1,
Ana-María González-Cuello
1,
Mario Barbagallo
3 and
Maria-Trinidad Herrero
1,* on behalf of GOING FWD Investigators
1
Clinical and Experimental Neuroscience (NiCE), Institute for Aging Research, Biomedical Institute for Bio-Health Research of Murcia (IMIB-Arrixaca), School of Medicine, Campus Mare Nostrum, UniWell, University of Murcia, 30100 Murcia, Spain
2
Department of Automation, Electrical Engineering and Electronic Technology, Campus Muralla del Mar, Technical University of Cartagena, 30202 Cartagena, Murcia, Spain
3
Geriatric Unit, Department of Medicine, University of Palermo, 90100 Palermo, Italy
4
Faculty of Medicine and Surgery, University of Enna “Kore”, 94100 Enna, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(22), 6794; https://doi.org/10.3390/jcm13226794
Submission received: 27 August 2024 / Revised: 31 October 2024 / Accepted: 7 November 2024 / Published: 12 November 2024
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Diagnosis in Clinical Practice)
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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.
Keywords: artificial intelligence; machine learning; decision tree; sarcopenia; English Longitudinal Study of Ageing; older adults; epidemiology; aging; cohort; prospective artificial intelligence; machine learning; decision tree; sarcopenia; English Longitudinal Study of Ageing; older adults; epidemiology; aging; cohort; prospective

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MDPI and ACS Style

Pavón-Pulido, N.; Dominguez, L.; Blasco-García, J.D.; Veronese, N.; Lucas-Ochoa, A.-M.; Fernández-Villalba, E.; González-Cuello, A.-M.; Barbagallo, M.; Herrero, M.-T., on behalf of GOING FWD Investigators. Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing. J. Clin. Med. 2024, 13, 6794. https://doi.org/10.3390/jcm13226794

AMA Style

Pavón-Pulido N, Dominguez L, Blasco-García JD, Veronese N, Lucas-Ochoa A-M, Fernández-Villalba E, González-Cuello A-M, Barbagallo M, Herrero M-T on behalf of GOING FWD Investigators. Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing. Journal of Clinical Medicine. 2024; 13(22):6794. https://doi.org/10.3390/jcm13226794

Chicago/Turabian Style

Pavón-Pulido, Nieves, Ligia Dominguez, Jesús Damián Blasco-García, Nicola Veronese, Ana-María Lucas-Ochoa, Emiliano Fernández-Villalba, Ana-María González-Cuello, Mario Barbagallo, and Maria-Trinidad Herrero on behalf of GOING FWD Investigators. 2024. "Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing" Journal of Clinical Medicine 13, no. 22: 6794. https://doi.org/10.3390/jcm13226794

APA Style

Pavón-Pulido, N., Dominguez, L., Blasco-García, J. D., Veronese, N., Lucas-Ochoa, A.-M., Fernández-Villalba, E., González-Cuello, A.-M., Barbagallo, M., & Herrero, M.-T., on behalf of GOING FWD Investigators. (2024). Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing. Journal of Clinical Medicine, 13(22), 6794. https://doi.org/10.3390/jcm13226794

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