IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i18p3275-d264732.html
   My bibliography  Save this article

Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital

Author

Listed:
  • Cristián Castillo-Olea

    (eVIDA Research Group, University of Deusto, 48007 Bilbo, Spain)

  • Begonya García-Zapirain Soto

    (eVIDA Research Group, University of Deusto, 48007 Bilbo, Spain)

  • Christian Carballo Lozano

    (eVIDA Research Group, University of Deusto, 48007 Bilbo, Spain)

  • Clemente Zuñiga

    (Geriatric, Tijuana General Hospital, Tijuana 22195, Mexico)

Abstract

This 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.

Suggested Citation

  • Cristián Castillo-Olea & Begonya García-Zapirain Soto & Christian Carballo Lozano & Clemente Zuñiga, 2019. "Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital," IJERPH, MDPI, vol. 16(18), pages 1-10, September.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3275-:d:264732
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/18/3275/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/18/3275/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meelan Thondoo & David Rojas-Rueda & Joyeeta Gupta & Daniel H. de Vries & Mark J. Nieuwenhuijsen, 2019. "Systematic Literature Review of Health Impact Assessments in Low and Middle-Income Countries," IJERPH, MDPI, vol. 16(11), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Débora Cynamon Kligerman & Telma Abdalla de Oliveira Cardoso & Simone Cynamon Cohen & Déborah Chein Bueno de Azevedo & Graziella de Araújo Toledo & Ana Paula Chein Bueno de Azevedo & Susanne M. Charle, 2022. "Methodology for a Comprehensive Health Impact Assessment in Water Supply and Sanitation Programmes for Brazil," IJERPH, MDPI, vol. 19(19), pages 1-26, October.
    2. Isaac Lyatuu & Georg Loss & Andrea Farnham & Goodluck W. Lyatuu & Günther Fink & Mirko S. Winkler, 2021. "Associations between Natural Resource Extraction and Incidence of Acute and Chronic Health Conditions: Evidence from Tanzania," IJERPH, MDPI, vol. 18(11), pages 1-12, June.
    3. Dominik Dietler & Ruth Lewinski & Sophie Azevedo & Rebecca Engebretsen & Fritz Brugger & Jürg Utzinger & Mirko S. Winkler, 2020. "Inclusion of Health in Impact Assessment: A Review of Current Practice in Sub-Saharan Africa," IJERPH, MDPI, vol. 17(11), pages 1-20, June.
    4. Zhan S. Kalel & Gabriel Gulis & Altyn M. Aringazina, 2023. "Implementation of Health Impact Assessment in the Healthcare System of the Republic of Kazakhstan," IJERPH, MDPI, vol. 20(3), pages 1-12, January.
    5. Françoise Jabot & Emile Tremblay & Ana Rivadeneyra & Thierno Amadou Diallo & Geneviève Lapointe, 2020. "A Comparative Analysis of Health Impact Assessment Implementation Models in the Regions of Montérégie (Québec, Canada) and Nouvelle-Aquitaine (France)," IJERPH, MDPI, vol. 17(18), pages 1-18, September.
    6. Meelan Thondoo & Daniel H. De Vries & David Rojas-Rueda & Yashila D. Ramkalam & Ersilia Verlinghieri & Joyeeta Gupta & Mark J. Nieuwenhuijsen, 2020. "Framework for Participatory Quantitative Health Impact Assessment in Low- and Middle-Income Countries," IJERPH, MDPI, vol. 17(20), pages 1-20, October.
    7. Guilhem Dardier & Derek P. T. H. Christie & Jean Simos & Anne Roué Le Gall & Nicola L. Cantoreggi & Lorris Tabbone & Yoann Mallet & Françoise Jabot, 2023. "Health Impact Assessment to Promote Urban Health: A Trans-Disciplinary Case Study in Strasbourg, France," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3275-:d:264732. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.