Abstract
| Original language | English |
|---|---|
| Publication status | Accepted/In press - 4 Jan 2019 |
| Event | International 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
| Conference | International Conference on Risk Analysis and Hazard Mitigation |
|---|---|
| Abbreviated title | ICRAHM |
| Country | Sweden |
| City | Stockholm |
| Period | 15/07/19 → 16/07/19 |
| Internet address |
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Keywords
- Classification
- Falls
- Health Risk Factors
- Machine Learning
- Older Adults
Cite this
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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 conference › Paper
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 -