IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2024i1p98-d1555988.html
   My bibliography  Save this article

Feature Selection and Machine Learning Approaches for Detecting Sarcopenia Through Predictive Modeling

Author

Listed:
  • Akhrorbek Tukhtaev

    (Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea)

  • Dilmurod Turimov

    (Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea)

  • Jiyoun Kim

    (Department of Exercise Rehabilitation & Welfare, Gachon University, Incheon 21936, Republic of Korea)

  • Wooseong Kim

    (Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea)

Abstract

Sarcopenia is an age-associated condition characterized by a muscle mass and function decline. This condition poses significant health risks for the elderly. This study developed a machine-learning model to predict sarcopenia using data from 664 participants. Key features were identified using the Local Interpretable Model-Agnostic Explanations (LIME) method. This enhanced model interpretability. Additionally, the CatBoost algorithm was used for training, and SMOTE-Tomek addressed dataset imbalance. Notably, the reduced-feature model outperformed the full-feature model, achieving an accuracy of 0.89 and an AUC of 0.94. The results highlight the importance of feature selection for improving model efficiency and interpretability in clinical applications. This approach provides valuable insights into the early identification and management of sarcopenia, contributing to better patient outcomes.

Suggested Citation

  • Akhrorbek Tukhtaev & Dilmurod Turimov & Jiyoun Kim & Wooseong Kim, 2024. "Feature Selection and Machine Learning Approaches for Detecting Sarcopenia Through Predictive Modeling," Mathematics, MDPI, vol. 13(1), pages 1-26, December.
    • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:98-:d:1555988
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/1/98/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/1/98/
    Download Restriction: no
    ---><---

    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:jmathe:v:13:y:2024:i:1:p:98-:d:1555988. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.