Abstract：
Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps use Machine Learning (ML) to predict cognitive impairment. The aim of this work is to find explanations for the predictions of such a mobile application called DemPredict using methods from the field of Explainable Artificial Intelligence (XAI). In order to evaluate which method is best suited, different XAI approaches are used and compared. However, the comparability of the results is a key challenge. By evaluating the trustworthiness, stability, and computation time of the methods, it is possible to identify the optimal XAI approaches for the respective algorithms.
摘要：
最近,数字应用程序已经进入市场，通过提供数字痴呆症筛查来实现痴呆症的早期诊断。其中一些应用程序使用机器学习(ML)来预测认知障碍。这项工作的目的是使用可解释人工智能（XAI）领域的方法，找到对称为DemPredict的移动应用程序预测的解释。为了评估哪种方法最适合，使用和比较了不同的XAI方法。然而,结果的可比性是一个关键挑战。通过评估可信度，稳定性,和方法的计算时间，可以为相应的算法确定最佳的XAI方法。
