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Self-Organizing Maps to Multidimensionally Characterize Physical Profiles in Older Adults

Author

Listed:
  • Lorena Parra-Rodríguez

    (Research Department, Instituto Nacional de Geriatría, Mexico City 10200, Mexico)

  • Edward Reyes-Ramírez

    (Research Department, Instituto Nacional de Geriatría, Mexico City 10200, Mexico)

  • José Luis Jiménez-Andrade

    (Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
    Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
    Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación, INFOTEC, Mexico City 14050, Mexico)

  • Humberto Carrillo-Calvet

    (Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
    Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico)

  • Carmen García-Peña

    (Research Department, Instituto Nacional de Geriatría, Mexico City 10200, Mexico)

Abstract

The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City that participated in this study. Data regarding demographics, geriatric syndromes, comorbidities, physical performance, and body composition were obtained. The sample was divided by sex, and the multidimensional analysis included age, gait speed over height, grip strength over body mass index, one-legged stance, lean appendicular mass percentage, and fat percentage. Using the SOM neural network, seven profile types for older men and women were identified. This analysis provided maps depicting a set of clusters qualitatively characterizing groups of older adults that share similar profiles of body composition and physical performance. The SOM neural network proved to be a useful tool for analyzing multidimensional health care data and facilitating its interpretability. It provided a visual representation of the non-linear relationship between physical performance and body composition variables, as well as the identification of seven characteristic profiles in this cohort.

Suggested Citation

  • Lorena Parra-Rodríguez & Edward Reyes-Ramírez & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet & Carmen García-Peña, 2022. "Self-Organizing Maps to Multidimensionally Characterize Physical Profiles in Older Adults," IJERPH, MDPI, vol. 19(19), pages 1-25, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12412-:d:928959
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    References listed on IDEAS

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    1. Nawapong Chumha & Sujitra Funsueb & Sila Kittiwachana & Pimonpan Rattanapattanakul & Peerasak Lerttrakarnnon, 2020. "An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population," IJERPH, MDPI, vol. 17(18), pages 1-12, September.
    2. Elio Atenógenes Villaseñor & Ricardo Arencibia-Jorge & Humberto Carrillo-Calvet, 2017. "Multiparametric characterization of scientometric performance profiles assisted by neural networks: a study of Mexican higher education institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 77-104, January.
    3. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Multidimensional Data Visualization," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4419-0236-8, December.
    4. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Multidimensional Data and the Concept of Visualization," Springer Optimization and Its Applications, in: Multidimensional Data Visualization, edition 127, chapter 0, pages 1-4, Springer.
    5. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Strategies for Multidimensional Data Visualization," Springer Optimization and Its Applications, in: Multidimensional Data Visualization, edition 127, chapter 0, pages 5-40, Springer.
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