Explainable artificial intelligence for falls prediction

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)
209 Downloads (Pure)


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
EditorsMayank Singh, P.K. Gupta, Vipin Tyagi, Jan Flusser, Tuncer Ören, Gianluca Valentino
PublisherSpringer Nature, Singapore
Number of pages9
ISBN (Electronic)978-981-15-6634-9
ISBN (Print)978-981-15-6633-2
Publication statusPublished (in print/issue) - 15 Aug 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)


Conference4th International Conference on Advances in Computing and Data Sciences
Abbreviated titleICACDS 2020
Internet address


  • predictive analytics
  • health and social care
  • older people
  • risk assessment
  • Human-computer Interfacing
  • Falls
  • Risks
  • Explainable AI
  • Classification
  • Decision tree


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