Chen Tianhua, Su Pan, Shen Yinghua, Chen Lu, Mahmud Mufti, Zhao Yitian, Antoniou Grigoris
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.
School of Control and Computer Engineering, North China Electric Power University, Beijing, China.
Front Neurosci. 2022 Aug 1;16:867664. doi: 10.3389/fnins.2022.867664. eCollection 2022.
Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.
痴呆症是一种主要影响老年人群的无法治愈的神经退行性疾病，世界卫生组织已将促进早期诊断和及时管理作为痴呆症护理的主要目标之一。虽然一系列流行的机器学习算法及其变体已被应用于痴呆症诊断，但模糊系统在处理不确定性方面已知有效，并能明确说明如何推断诊断，在最近的文献中偶尔出现。鉴于基于模糊规则的模型的优势，它有可能产生一个临床决策支持系统，提供可理解的规则和透明的推理过程来支持痴呆症诊断，本文通过采用源于图论研究的支配集概念，提出了一种新颖的模糊推理系统。一种剥离策略用于从构建的边加权图中迭代提取一组支配集。每个支配集进一步转换为参数化模糊规则，最终在基于监督自适应网络的模糊推理框架中进行优化。提供了一个示例，展示了可解释的规则和达成决策的透明推理过程。对来自开放获取影像研究系列（OASIS）存储库的数据进行的进一步系统实验，也验证了其相对于其他方法的优越性能。
