FEATURESPredicting Falls Among Community-Dwelling Older Adults A Demonstration of Applied Machine LearningYang, Rumei PhD, RN; Plasek, Joseph M. PhD; Cummins, Mollie R. PhD, RN, FAAN, FACMI; Sward, Katherine A. PhD, RN, FAANAuthor Information Author Affiliations: School of Nursing, Nanjing Medical University (Dr Yang), Nanjing, Jiangsu, China; and College of Nursing (Drs Yang, Cummins, and Sward) and Department of Biomedical Informatics, School of Medicine (Dr Plasek), University of Utah, Salt Lake City. R.Y. and J.M.P. contributed equally to this work. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Funding: Nanjing Medical University, Nanjing, China, JX10631803 & NMUR2020006. National Natural Science Foundation of China, grant number 72004098. Corresponding author: Rumei Yang, PhD, RN, School of Nursing, Nanjing Medical University, 101 Longmian Ave, Jiangning District, Jiangsu, Nanjing, China 211166; and University of Utah College of Nursing 10 S 2000 E, Salt Lake City, UT 84112 ([email protected]; [email protected]n). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.cinjournal.com). CIN: Computers, Informatics, Nursing: May 2021 - Volume 39 - Issue 5 - p 273-280 doi: 10.1097/CIN.0000000000000688 Buy SDC Metrics Abstract Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.