Using Explainable AI (XAI) for the Prediction of Falls in the Older Population

Abstract

The prevention of falls in older people requires the identification of the most important risk factors. Frailty is associated with risk of falls, but not all falls are of the same nature. In this work, we utilised data from The Irish Longitudinal Study on Ageing to implement Random Forests and Explainable Artificial Intelligence (XAI) techniques for the prediction of different types of falls and analysed their contributory factors using 46 input features that included those of a previously investigated frailty index. Data of participants aged 65 years and older were fed into four random forest models (all falls or syncope, simple fall, complex fall, and syncope). Feature importance rankings were based on mean decrease in impurity, and Shapley additive explanations values were calculated and visualised. Female sex and a previous fall were found to be of high importance in all of the models, and polypharmacy (being on five or more regular medications) was ranked high in the syncope model. The more ‘accidental’ (extrinsic) nature of simple falls was demonstrated in its model, where the presence of many frailty features had negative model contributions. Our results highlight that falls in older people are heterogenous and XAI can provide new insights to help their prevention.

Keywords

• explainable artificial intelligence
• random forests
• falls
• frailty
• healthcare