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基于BP神经网络和支持向量机的荨麻疹证候分类探讨

Exploration of Classification of Syndrome Patterns of Urticaria Based on BP Neural Network and Support Vector Machine
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摘要 从知网、万方、维普等数据库中收集临床中医治疗荨麻疹有效的案例资料,基于BP神经网络和支持向量机模型探讨构建荨麻疹证候分类模型,并比较分析两种证候分类模型的精确度。结果显示,BP神经网络证候分类模型测试的准确率为83.13%,支持向量机证候分类模型测试准确率为92.3%,支持向量机证候分类模型的精确度高于BP神经网络证候分类模型。提示利用BP神经网络与支持向量机分类器进行荨麻疹证候分类均可取得较好的结果。但因本研究尚处在理论模型探讨阶段,故其准确度仍需更大的样本量及智能优化算法等以进一步提高。 Data of the effective cases treated by Chinese medicine were collected from the databases of CNKI,Wanfang and VIP for the classification of traditional Chinese medicine syndrome patterns of urticaria based on the models of BP(back propagation)neural network and support vector machine(SVM),and then the accuracy of BP neural network syndrome classifier and SVM syndrome classifier was compared. The results showed that BP neural network syndrome classifier had the accuracy of 83.13% and SVM syndrome classifier had the accuracy of 92.3%,indicating that the latter one had the higher accuracy. Both of BP neural network syndrome classifier and SVM syndrome classifier can achieve satisfactory effect for the syndrome classification of urticaria. For the research is just in the exploration stage of theoretical model,large sample size and intelligent optimization algorithm will be needed to increase the accuracy of BP neural network syndrome classifier and SVM syndrome classifier.
作者 刘丽蓉 詹秀菊 LIU Li-Rong;ZHAN Xiu-Ju(School of Medical Information Engineering,Guangzhou University of Chinese Medicine,Guangzhou 510006 Guangdong,China)
出处 《广州中医药大学学报》 CAS 2020年第3期573-577,共5页 Journal of Guangzhou University of Traditional Chinese Medicine
关键词 BP神经网络 支持向量机 荨麻疹 证候分类 BP neural network support vector machine urticaria syndrome classification
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