Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults

Leeanne Lindsay, Sonya Coleman, Dermot Kerr, Brian Taylor, Anne Moorhead

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Abstract

The aim of this study is to examine different cognitive health risk factors that could possibly be contributing to the risk of falling within older adults in the Republic of Ireland (ROI). Possible health risk factors associated with older adults and those with dementia were derived from The Irish Longitudinal Study on Ageing dataset and were used with a machine learning approach to predict if they can be contributed towards falls. This study involved a secondary data analysis as there was no direct contact with respondents and all data was anonymized before analyzing. Risk factors were originally taken from a previous project ‘Risk Communication in Dementia’, whereby risk factors were identified and can now be used in a data analytic approach to predict risk and minimize harm. Using health risk factors such as overall health, mental health, long-term health conditions, blackouts, fainting and joint replacements, these have been proven to contribute to the risk of falling
Original languageEnglish
Publication statusAccepted/In press - 4 Jan 2019
EventInternational Conference on Risk Analysis and Hazard Mitigation - Stockholm, Sweden
Duration: 15 Jul 2019 → 16 Jul 2019
https://waset.org/apply/2019/07/stockholm/ICRAHM?step=1#papers

Conference

ConferenceInternational Conference on Risk Analysis and Hazard Mitigation
Abbreviated titleICRAHM
CountrySweden
CityStockholm
Period15/07/19 → 16/07/19
Internet address

Keywords

  • Classification
  • Falls
  • Health Risk Factors
  • Machine Learning
  • Older Adults

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    Lindsay, L., Coleman, S., Kerr, D., Taylor, B., & Moorhead, A. (Accepted/In press). Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults. Paper presented at International Conference on Risk Analysis and Hazard Mitigation, Stockholm, Sweden.