Abstract
With a rapidly ageing population, it is likely that we will en-counter an older adult falling. Falls can cause death, serious injury or harm, loss of confidence and loss of independence. Falling can happen to any of us, however those over 65 years of age can be classified as a group of adults who are more vulnerable and at increased risk of falling. This paper focuses on applying explainable artificial intelligence techniques, in the form of decision trees, to healthcare data in order to predict the risk of falling in older adults. These decision trees could potentially be introduced for health and social care professionals to help aid their judgements when making decisions.
Original language | English |
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Title of host publication | 4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020 |
Editors | Mayank Singh, P.K. Gupta, Vipin Tyagi, Jan Flusser, Tuncer Ören, Gianluca Valentino |
Publisher | Springer Nature, Singapore |
Chapter | 8 |
Pages | 76-84 |
Number of pages | 9 |
ISBN (Electronic) | 978-981-15-6634-9 |
ISBN (Print) | 978-981-15-6633-2 |
DOIs | |
Publication status | Published (in print/issue) - 15 Aug 2020 |
Event | 4th International Conference on Advances in Computing and Data Sciences - University of Malta, Valleta, Malta Duration: 24 Apr 2020 → 25 Apr 2020 https://link.springer.com/book/10.1007/978-981-15-6634-9 (Link to conference website) |
Conference
Conference | 4th International Conference on Advances in Computing and Data Sciences |
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Abbreviated title | ICACDS 2020 |
Country/Territory | Malta |
City | Valleta |
Period | 24/04/20 → 25/04/20 |
Internet address |
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Keywords
- predictive analytics
- health and social care
- older people
- risk assessment
- Human-computer Interfacing
- Falls
- Risks
- Explainable AI
- Classification
- Decision tree