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

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Author(s)
Soo-Kyoung LeeJuh Hyun ShinJinhyun AhnJi Yeon LeeDong Eun Jang
Keimyung Author(s)
Lee, Soo Kyoung
Department
Dept. of Nursing (간호학)
Journal Title
Int J Environ Res Public Health
Issued Date
2021
Volume
18
Issue
6
Keyword
pressure ulcersmachine learningnursing home
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.
Keimyung Author(s)(Kor)
이수경
Publisher
College of Nursing (간호대학)
Citation
Soo-Kyoung Lee et al. (2021). Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods. Int J Environ Res Public Health, 18(6), 2954. doi: 10.3390/ijerph18062954
Type
Article
ISSN
1660-4601
Source
https://www.mdpi.com/1660-4601/18/6/2954
DOI
10.3390/ijerph18062954
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43745
Appears in Collections:
2. College of Nursing (간호대학) > Dept. of Nursing (간호학)
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