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Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment. Participants were further classified into four subgroups based on fall risk: controls, low-risk MS (n\u2009=\u200934), moderate-risk MS (n\u2009=\u200927), high-risk MS (n\u2009=\u200942). Twenty common sway metrics were derived following standard procedures and subsequently used to train a machine learning algorithm (random forest \u2013 RF, with 10-fold cross validation) to predict individuals\u2019 fall risk grouping. The sway-metric based RF classifier had high accuracy in discriminating controls from MS individuals (&gt;86%). Sway sample entropy was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings.<\/jats:p>","DOI":"10.1038\/s41598-019-52697-2","type":"journal-article","created":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T06:03:34Z","timestamp":1573020214000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach"],"prefix":"10.1038","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0738-3721","authenticated-orcid":false,"given":"Ruopeng","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine L.","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob J.","family":"Sosnoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"52697_CR1","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1212\/WNL.0000000000000768","volume":"83","author":"P Browne","year":"2014","unstructured":"Browne, P. et al. 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