Application of machine learning methods in nursing home research

Soo Kyoung Lee, Jinhyun Ahn, Juh Hyun Shin, Ji Yeon Lee

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Article number6234
Pages (from-to)1-15
Number of pages15
JournalInternational Journal of Environmental Research and Public Health
Volume17
Issue number17
DOIs
StatePublished - 1 Sep 2020

Keywords

  • Accidental falls
  • Machine learning
  • Nursing homes

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