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Deduction research on syndromes diagnosis of TCM Inquiry for Cardiovascular Diseases based on RBF nerve network

机译:基于RBF神经网络的心血管病中医证候诊断推论研究

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In this study, wo collected Cases by means of uniform Information- Collection Scale of Inquiry Diagnosis for Cardiovascular Diseases in TCM. The diagnostic criteria were established, and each case was interpreted by associate chief physician or senior doctor. The value “1 or 0” was assigned to “with or without” information. By using Epidate 3.1, the information collected was entered by two persons and twice each, and the database was established. Dialectical deduction research of inquiry diagnosis for cardiovascular diseases on the basis of RBF nerve network was conducted. The RBF nerve network parameters were determined after many experiments: (1) velocity was between 0.1–5; (2) target error was 0.00001. The result showed that the overall precision rate was as high as 88.86% for identification of syndromes of cardiovascular diseases in TCM on the basis of RBF nerve network. The precision rate for RBF nerve network to recognize syndromes of TCM was rather high, which could provide theoretical and technical support for the objectivity and standardization study of syndromes of TCM.
机译:在这项研究中,我们通过统一的中医心血管疾病问诊诊断信息收集量表收集病例。确定诊断标准,并由副主任医师或高级医生对每例病例进行解释。将值“ 1或0”分配给“有或没有”信息。通过使用Epidate 3.1,收集的信息由两个人输入,每个人输入两次,并建立了数据库。基于RBF神经网络进行了心血管疾病询问诊断的辩证推导研究。经过多次实验确定了RBF神经网络参数:(1)速度在0.1–5之间; (2)目标误差为0.00001。结果表明,基于RBF神经网络的中医心血管疾病综合征的综合识别准确率高达88.86%。 RBF神经网络识别中医证候的准确率较高,可为中医证候的客观性和标准化研究提供理论和技术支持。

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