# 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. explaibnable artificial intelligence; random forests; falls; frailty; healthcare Article Using Explainable AI (XAI) for the Prediction of Falls in the Older Population  
[PERSON]; [PERSON] Using Explainable AI (XAI) for the Prediction of Falls in the Older Population  
1 School of Medicine, Trinity College Dublin, D02 R590 Dublin, Ireland  
2  
Mercer's Institute for Successful Ageing, St. James's Hospital, D08 N0H Dublin, IrelandExplainable AI, or XAI, is used to describe a set of techniques that is aimed at making the results of AI models more comprehensible to its human users [12]. AI models have been commonly classified into white box and black box, with white box models such as Random Forest and Decision Trees being easier to understand by the end-users, and black box models such as Neural Networks being more difficult to explain in terms of their inner workings and how the final outputs are derived [13]. There is potential value in XAI within the healthcare sector, where understanding how AI algorithms handle data inputs is crucial in ensuring the trustworthiness of their outputs for real-life clinical decision making [14]. Indeed, explainability can facilitate the implementation of AI in healthcare [15]. A previous falls prediction effort that was based on the simple arithmetic sum of individual health deficits (frailty index) offered significant results but with low degree of explainability [6]. However, by implementing XAI, new insights may be obtained through us being able to understand which features are the most important, and how they influence the prediction outcome. The transparency of the model's behaviour also allows for error correction and the improvement of the model performance. These are crucial in the healthcare field where inaccurate clinical decisions could lead to adverse consequences for patients. Moreover, with the increasing implementation of AI in healthcare settings, compliance with the European General Data Protection Regulation (GDPR) is ever more important. In that regard, compared to black box algorithms, XAI techniques are better placed to support the compliance with the \"right to explanation\", which is the right to be given an explanation for the output of an algorithm [14; 16]. With the ability to process large amounts of data and perform complex computational tasks, and backed by the insurance of electronic health records and other large health databases, there are increasing opportunities for AI to be implemented within both the research and clinical fields of medicine.