A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women

• Emily Gregg ,

  Roles Data curation, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: emily.gregg@york.ac.uk (EG); g.nicholson@leedsbeckett.ac.uk (GN)

  Affiliations Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom, York Health Economics Consortium, University of York, York, United Kingdom

  https://orcid.org/0009-0003-2969-793X

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• Clive Beggs,

  Roles Conceptualization, Data curation, Investigation, Software, Supervision, Visualization, Writing – review & editing

  Affiliations Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom, Department of Medicine for the Elderly, Cambridge University Hospitals, Cambridge, United Kingdom

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• Athanassios Bissas,

  Roles Conceptualization, Methodology, Supervision, Writing – review & editing

  Affiliation School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom

  https://orcid.org/0000-0002-7858-9623

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• Gareth Nicholson

  Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

  * E-mail: emily.gregg@york.ac.uk (EG); g.nicholson@leedsbeckett.ac.uk (GN)

  Affiliation Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom

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