IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i6p2954-d516522.html
   My bibliography  Save this article

Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods

Author

Listed:
  • Soo-Kyoung Lee

    (College of Nursing, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea)

  • Juh Hyun Shin

    (College of Nursing, Ewha Womans University, Science & Ewha Research Institute of Nursing Science, Seoul 120750, Korea)

  • Jinhyun Ahn

    (Department of Management Information Systems, Jeju National University, Jeju 63243, Korea)

  • Ji Yeon Lee

    (College of Nursing, Catholic University of Pusan, Busan 46252, Korea)

  • Dong Eun Jang

    (School of Nursing, University of Texas at Austin, Austin, TX 78712, USA)

Abstract

Background: Machine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions. Objective: The purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs). Methods: We applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60). Results: The random forest model showed the greatest accuracy (0.814), followed by logistic regression (0.782), polynomial SVM (0.779), radial SVM (0.770), linear SVM (0.767), and sigmoid SVM (0.674). Conclusions: The random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents’ characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.

Suggested Citation

  • Soo-Kyoung Lee & Juh Hyun Shin & Jinhyun Ahn & Ji Yeon Lee & Dong Eun Jang, 2021. "Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods," IJERPH, MDPI, vol. 18(6), pages 1-8, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:2954-:d:516522
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/6/2954/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/6/2954/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soo-Kyoung Lee & Jinhyun Ahn & Juh Hyun Shin & Ji Yeon Lee, 2020. "Application of Machine Learning Methods in Nursing Home Research," IJERPH, MDPI, vol. 17(17), pages 1-15, August.
    2. Juh Hyun Shin & Rosemary Anne Renaut & Mark Reiser & Ji Yeon Lee & Ty Yi Tang, 2021. "Increasing Registered Nurse Hours Per Resident Day for Improved Nursing Home Residents’ Outcomes Using a Longitudinal Study," IJERPH, MDPI, vol. 18(2), pages 1-11, January.
    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. Ryuichi Ohta & Yoshinori Ryu & Chiaki Sano, 2021. "Effects of Implementation of Infection Control Measures against COVID-19 on the Condition of Japanese Rural Nursing Homes," IJERPH, MDPI, vol. 18(11), pages 1-9, 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:18:y:2021:i:6:p:2954-:d:516522. 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.