首页> 外文会议>2009 IEEE International Symposium on IT in Medicine Education( IEEE 教育与医药信息化国际会议)论文集 >Research of TCM Syndromes Diagnostic Models for Chronic Gastritis Based On Multielement Mathematical Statistical Methods
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Research of TCM Syndromes Diagnostic Models for Chronic Gastritis Based On Multielement Mathematical Statistical Methods

机译:基于多元数学统计方法的慢性胃炎中医证候诊断模型研究

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In this study, we assessed the large sample population of patients with chronic gastritis based on three methods with supervised learning function, i.e., the regression analysis, BP neural network and Support Vector Machine. On basis of the results, we constructed the diagnostic models to predict the types of Traditional Chinese Medicine (TCM) syndromes of chronic gastritis, and compared the correct rate and applicability of each method. The study showed the correct rate of prediction was as follows: Support Vector Machine > BP neural network > regression analysis, after construction of diagnostic models with three algorithms. We believe, our results could be of great value in exploring the methodology of objectification and standardization of TCM Syndromes.
机译:在这项研究中,我们基于三种具有监督学习功能的方法(即回归分析,BP神经网络和支持向量机)评估了慢性胃炎患者的大量样本。根据结果​​,我们构建了诊断模型以预测慢性胃炎的中医证候类型,并比较了每种方法的正确率和适用性。研究表明正确的预测率如下:在使用三种算法构建诊断模型之后,支持向量机> BP神经网络>回归分析。我们相信,我们的结果可能对探索中医证候的客观化和标准化方法具有重要价值。

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