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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Explainable Artificial Intelligence for Falls Prediction
verfasst von : Leeanne Lindsay, Sonya Coleman, Dermot Kerr, Brian Taylor, Anne Moorhead
Erschienen in: Advances in Computing and Data Sciences
Verlag: Springer Singapore
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Abstract
With a rapidly ageing population, it is likely that we will encounter 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.