Application of Machine Learning Methods in Nursing Home Research

Soo-Kyoung Lee, Jinhyun Ahn, Juh Hyun Shin and Ji Yeon Lee
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Soo-Kyoung Lee: College of Nursing, Keimyung University, 1095, Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea
Jinhyun Ahn: Department of Management Information Systems, Jeju National University, Jeju-do 63243, Korea
Juh Hyun Shin: College of Nursing, Ewha Womans University, Seoul 03760, Korea
Ji Yeon Lee: College of Nursing, Ewha Womans University, Seoul 03760, Korea

IJERPH, 2020, vol. 17, issue 17, 1-15

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.

Keywords: machine learning; accidental falls; nursing homes (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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