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首页> 外文期刊>International journal of knowledge engineering and soft data paradigms >An intelligent model for two level diagnoses of neuromuscular diseases
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An intelligent model for two level diagnoses of neuromuscular diseases

机译:神经肌肉疾病二级诊断的智能模型

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In this paper, we have developed a intelligent model that performs two level diagnosis of neuromuscular diseases (NMDs) by using muscular, cognitive, psychological, internal lingual and EMG parameters. At first level, the NMDs are: muscular dystrophy, polymyositis, endocrine myopathy, metabolic myopathy, neuropathy, poliomyelitis and myasthenia gravis. These diseases are further classified in more specific diseases at second level. Data mining methods are used to obtain the important parameters/symptoms. A hierarchal rule base model is integrated with case base reasoning for calculating cumulative confidence factor (CCF) and confidence index. In addition to this a Bayesian network (BN) model is also developed for calculating the probability of occurrence of diseases and to make a comparative view of the results obtained by CCF and BN approaches.
机译:在本文中,我们开发了一种智能模型,该模型通过使用肌肉,认知,心理,内部语言和EMG参数对神经肌肉疾病(NMD)进行两级诊断。首先,NMD为:肌营养不良,多发性肌炎,内分泌肌病,代谢性肌病,神经病,脊髓灰质炎和重症肌无力。这些疾病在第二级进一步分类为更具体的疾病。数据挖掘方法用于获取重要的参数/症状。层次规则库模型与案例库推理相结合,用于计算累积置信度(CCF)和置信度指数。除此之外,还开发了贝叶斯网络(BN)模型,用于计算疾病发生的可能性并比较通过CCF和BN方法获得的结果。

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