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Application of Machine Learning Methods in Nursing Home Research
Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, lin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503291/ https://www.ncbi.nlm.nih.gov/pubmed/32867250 http://dx.doi.org/10.3390/ijerph17176234 |
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author | Lee, Soo-Kyoung Ahn, Jinhyun Shin, Juh Hyun Lee, Ji Yeon |
author_facet | Lee, Soo-Kyoung Ahn, Jinhyun Shin, Juh Hyun Lee, Ji Yeon |
author_sort | Lee, Soo-Kyoung |
collection | PubMed |
description | Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods. |
format | Online Article Text |
id | pubmed-7503291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75032912020-09-23 Application of Machine Learning Methods in Nursing Home Research Lee, Soo-Kyoung Ahn, Jinhyun Shin, Juh Hyun Lee, Ji Yeon Int J Environ Res Public Health Article Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods. MDPI 2020-08-27 2020-09 /pmc/articles/PMC7503291/ /pubmed/32867250 http://dx.doi.org/10.3390/ijerph17176234 Text en © 2020 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 Ahn, Jinhyun Shin, Juh Hyun Lee, Ji Yeon Application of Machine Learning Methods in Nursing Home Research |
title | Application of Machine Learning Methods in Nursing Home Research |
title_full | Application of Machine Learning Methods in Nursing Home Research |
title_fullStr | Application of Machine Learning Methods in Nursing Home Research |
title_full_unstemmed | Application of Machine Learning Methods in Nursing Home Research |
title_short | Application of Machine Learning Methods in Nursing Home Research |
title_sort | application of machine learning methods in nursing home research |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503291/ https://www.ncbi.nlm.nih.gov/pubmed/32867250 http://dx.doi.org/10.3390/ijerph17176234 |
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