The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐ neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research.

中文翻译：

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选择最合适的分类算法以支持痴呆症患者的辅助技术采用：多标准框架

痴呆症（PwD）的人数正在急剧增加。PwD表现出推理，记忆和思想上的缺陷，需要某种形式的自我管理干预来支持日常活动的完成，同时保持一定的独立性。为了满足这一需求，人们一直致力于开发辅助技术解决方案，这可以提供一个减轻PwD及其护理人员所面临负担的机会。然而，这种解决方案的采用受到限制。因此，有必要使用分类器来区分这些技术的采用者和非采用者，以避免成本超支和对生活质量的潜在负面影响。随着多种分类算法的发展，选择最合适的分类器已成为采用技术的关键一步。为了选择最合适的分类器，决策者需要考虑来自各个领域的一系列标准。此外，至关重要的是为技术采用建模定义最合适的多准则决策方法。考虑到上述方面，本文提出了一种基于模糊分析层次过程的五阶段方法与基于理想解的相似性偏好排序技术的集成，以确定支持辅助技术采用研究的最合适分类器。采用模糊层次分析法确定不确定性条件下准则和子准则的相对权重，并采用与理想解相似的优先顺序技术对分类器的备选方案进行排序。描述了一个案例研究，该案例考虑了针对PwD的基于移动的自我管理和提醒解决方案，以验证所提出的方法。结果表明，最佳分类器为k最近邻，其紧密系数为0.804，选择分类器时最重要的标准是可伸缩性。本文还讨论了未来研究中应解决的每种算法的优缺点。描述了一个案例研究，该案例考虑了针对PwD的基于移动的自我管理和提醒解决方案，以验证所提出的方法。结果表明，最佳分类器为k最近邻，其紧密系数为0.804，选择分类器时最重要的标准是可伸缩性。本文还讨论了未来研究中应解决的每种算法的优缺点。描述了一个案例研究，该案例考虑了针对PwD的基于移动的自我管理和提醒解决方案，以验证所提出的方法。结果表明，最佳分类器为k最近邻，其紧密系数为0.804，选择分类器时最重要的标准是可伸缩性。本文还讨论了未来研究中应解决的每种算法的优缺点。