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首页> 外文期刊>Journal of biomedical informatics. >A hierarchical fuzzy rule-based approach to aphasia diagnosis.
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A hierarchical fuzzy rule-based approach to aphasia diagnosis.

机译：基于分层模糊规则的失语症诊断方法。

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摘要

Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with aback propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.
机译：由于语言的不确定性和模糊性，失语症候群的定义不一致，测试对象的大量测量不精确，自然多样性和主观性以及诊断疾病的专家的意见，失语诊断是一项特别具有挑战性的医学诊断任务。为了有效地解决该诊断过程，此处提出了一种基于层次模糊规则的结构，该结构通过统计分析在失语的构造中考虑了失语的不同特征的影响。由于其模糊和层次推理构造，该方法对于失语症和其他医学诊断应用程序可能是有效的。最初，对每个由不同特征组成的疾病症状进行统计分析。然后，将从训练集中测得的统计参数用于定义隶属函数和模糊规则。然后将所得的基于两层模糊规则的系统与反向传播的前馈神经网络进行比较，以诊断四种失语症类型：Anomic，Broca，Global和Wernicke。为了减少所需输入的数量，该技术在综合语音测试和自发语音测试中均得到了应用和比较。统计t检验分析证实，所提出的方法使用较少的失语症特征，同时在准确性方面也有显着提高。

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