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Application of Machine Learning Methods in Nursing Home Research

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Author(s)
Soo-Kyoung LeeJinhyun AhnJuh Hyun Shinand Ji Yeon Lee
Keimyung Author(s)
Lee, Soo Kyoung
Department
Dept. of Nursing (간호학)
Journal Title
International Journal of Environmental Research and Public Health
Issued Date
2020
Volume
17
Issue
17
Keyword
machine learningaccidental fallsnursing homes
Abstract
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.
Keimyung Author(s)(Kor)
이수경
Publisher
College of Nursing (간호대학)
Citation
Soo-Kyoung Lee et al. (2020). Application of Machine Learning Methods in Nursing Home Research. International Journal of Environmental Research and Public Health, 17(17), 6234. doi: 10.3390/ijerph17176234
Type
Article
ISSN
1660-4601
Source
https://www.mdpi.com/1660-4601/17/17/6234
DOI
10.3390/ijerph17176234
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43108
Appears in Collections:
2. College of Nursing (간호대학) > Dept. of Nursing (간호학)
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