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Application of Machine Learning Methods in Nursing Home Research

Author

Listed:
  • 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)

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.

Suggested Citation

  • Soo-Kyoung Lee & Jinhyun Ahn & Juh Hyun Shin & Ji Yeon Lee, 2020. "Application of Machine Learning Methods in Nursing Home Research," IJERPH, MDPI, vol. 17(17), pages 1-15, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6234-:d:404997
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    References listed on IDEAS

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    1. Jacques-Henri Veyron & Patrick Friocourt & Olivier Jeanjean & Laurence Luquel & Nicolas Bonifas & Fabrice Denis & Joël Belmin, 2019. "Home care aides’ observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of c," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-13, August.
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    Cited by:

    1. Soo-Kyoung Lee & Juh Hyun Shin & Jinhyun Ahn & Ji Yeon Lee & Dong Eun Jang, 2021. "Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods," IJERPH, MDPI, vol. 18(6), pages 1-8, March.

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