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 languageEnglish
Title of host publication4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020
Publication statusAccepted/In press - 4 Mar 2020
Event4th International Conference on Advances in Computing and Data Sciences - University of Malta, Valleta, Malta
Duration: 24 Apr 202025 Apr 2020
https://link.springer.com/book/10.1007/978-981-15-6634-9 (Link to conference website)

Conference

Conference4th International Conference on Advances in Computing and Data Sciences
Abbreviated titleICACDS 2020
CountryMalta
CityValleta
Period24/04/2025/04/20
Internet address

Keywords

  • Decision Tree
  • Explainable AI
  • Classification Algorithm

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    Lindsay, L., Kerr, D., Coleman, S., Taylor, B., & Moorhead, A. (Accepted/In press). Explainable AI for Falls Prediction. In 4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020