Using Machine Learning Algorithms to Predict the Likelihood of Recurrent Falls in Older Adults

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Downloads (Pure)

Abstract

It is well known that older adults are more prone to falls due to their cognitive decline with the ageing process. Falls and the consequences of falls can have a huge impact on older adults and their families. Communication of such risks must involve the individual, their families and health professionals. This paper explores the use of machine learning algorithms to pre-dict if multiple risk factors including falling can lead to recurrent falls based on the data collected by The Irish Longitudinal Study on Ageing. Initial results shows that certain risk factors do in fact contribute to older adults falling such as overall health, mental and long-term health as well as blackouts and joint replacements.
Original languageEnglish
Title of host publication15th International Conference on Machine Learning and Data Mining
Pages1-5
Number of pages5
Publication statusPublished - 12 Jul 2019
Event15th International Conference on Machine Learning and Data Mining - New York, United States
Duration: 13 Jul 201918 Jul 2019
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=78944&copyownerid=83716

Conference

Conference15th International Conference on Machine Learning and Data Mining
Abbreviated titleMLDM 2019
CountryUnited States
CityNew York
Period13/07/1918/07/19
Internet address

Keywords

  • Falls
  • Machine Learning
  • Risk Factors

Fingerprint Dive into the research topics of 'Using Machine Learning Algorithms to Predict the Likelihood of Recurrent Falls in Older Adults'. Together they form a unique fingerprint.

  • Cite this