Application of Machine Learning Methods in Nursing Home Research
- 주제(키워드) machine learning , accidental falls , nursing homes
- 주제(기타) Environmental Sciences
- 주제(기타) Public, Environmental & Occupational Health
- 설명문(일반) [Lee, Soo-Kyoung] Keimyung Univ, Coll Nursing, 1095 Dalgubeol Daero, Daegu 42601, South Korea; [Ahn, Jinhyun] Jeju Natl Univ, Dept Management Informat Syst, Jeju Do 63243, South Korea; [Shin, Juh Hyun; Lee, Ji Yeon] Ewha Womans Univ, Coll Nursing, Seoul 03760, South Korea
- 등재 SCIE, SSCI, SCOPUS
- OA유형 gold, Green Published
- 발행기관 MDPI
- 발행년도 2020
- URI http://www.dcollection.net/handler/ewha/000000174516
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/ijerph17176234
- PubMed https://pubmed.ncbi.nlm.nih.gov/32867250
초록/요약
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.
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