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

Research output: Contribution to conferencePaper

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

Conference

ConferenceInternational Conference on Risk Analysis and Hazard Mitigation
Abbreviated titleICRAHM
CountrySweden
CityStockholm
Period15/07/1916/07/19
Internet address

Fingerprint

Health risks
health risk
dementia
Health
risk communication
secondary analysis
health
Ireland
republic
longitudinal study
data analysis
mental health
Learning systems
contact
Aging of materials
Communication
learning

Keywords

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

Cite this

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.
Lindsay, Leeanne ; Coleman, Sonya ; Kerr, Dermot ; Taylor, Brian ; Moorhead, Anne. / 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.
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author = "Leeanne Lindsay and Sonya Coleman and Dermot Kerr and Brian Taylor and Anne Moorhead",
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Lindsay, L, Coleman, S, Kerr, D, Taylor, B & Moorhead, A 2019, '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, 15/07/19 - 16/07/19, .

Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults. / Lindsay, Leeanne; Coleman, Sonya; Kerr, Dermot; Taylor, Brian; Moorhead, Anne.

2019. Paper presented at International Conference on Risk Analysis and Hazard Mitigation, Stockholm, Sweden.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults

AU - Lindsay, Leeanne

AU - Coleman, Sonya

AU - Kerr, Dermot

AU - Taylor, Brian

AU - Moorhead, Anne

PY - 2019/1/4

Y1 - 2019/1/4

N2 - 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

AB - 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

KW - Classification

KW - Falls

KW - Health Risk Factors

KW - Machine Learning

KW - Older Adults

M3 - Paper

ER -

Lindsay L, Coleman S, Kerr D, Taylor B, Moorhead A. Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults. 2019. Paper presented at International Conference on Risk Analysis and Hazard Mitigation, Stockholm, Sweden.