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

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), po...

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Autores principales: Lee, Soo-Kyoung, Shin, Juh Hyun, Ahn, Jinhyun, Lee, Ji Yeon, Jang, Dong Eun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001016/
https://www.ncbi.nlm.nih.gov/pubmed/33805798
http://dx.doi.org/10.3390/ijerph18062954
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author Lee, Soo-Kyoung
Shin, Juh Hyun
Ahn, Jinhyun
Lee, Ji Yeon
Jang, Dong Eun
author_facet Lee, Soo-Kyoung
Shin, Juh Hyun
Ahn, Jinhyun
Lee, Ji Yeon
Jang, Dong Eun
author_sort Lee, Soo-Kyoung
collection PubMed
description 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.
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spelling pubmed-80010162021-03-28 Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods Lee, Soo-Kyoung Shin, Juh Hyun Ahn, Jinhyun Lee, Ji Yeon Jang, Dong Eun Int J Environ Res Public Health Article 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. MDPI 2021-03-13 /pmc/articles/PMC8001016/ /pubmed/33805798 http://dx.doi.org/10.3390/ijerph18062954 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Soo-Kyoung
Shin, Juh Hyun
Ahn, Jinhyun
Lee, Ji Yeon
Jang, Dong Eun
Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
title Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
title_full Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
title_fullStr Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
title_full_unstemmed Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
title_short Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
title_sort identifying the risk factors associated with nursing home residents’ pressure ulcers using machine learning methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001016/
https://www.ncbi.nlm.nih.gov/pubmed/33805798
http://dx.doi.org/10.3390/ijerph18062954
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