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中文摘要：
为了对肝炎患者进行快速无创筛查,用主成分分析（PCA）和BP神经网络相结合的方法建立了健康人与病毒性肝炎患者的舌边归一化反射率的光谱识别模型.采集了健康人与病毒性肝炎患者舌边的光谱数据,进行归一化反射率预处理,从每类舌边中各获得36组光谱数据,利用主成分分析法进行聚类分析得到主成分得分值,选取代表原始变量所能提供的绝大部分信息的数据进行建模,在72个样本中随机抽取健康人和病毒性肝炎患者各26例作为建模样本,用余下的20个样本对该模型进行预测.设定预测偏差在±0.2内为预测正确,模型预测准确率达到100%.实验结果表明,用PCA-BP方法可以实现对健康人与病毒性肝炎患者进行快速、精确地分类识别,这对加强中医舌诊的客观化起到了良好的促进作用.
英文摘要：
To achieve non-invasive detection on hepatitis patients,a model was established for identification of this disease based on normalized data of reflective spectrum from both healthy adults and viral hepatitis suffers,which was processed by the method of principal component analysis（PCA）and BP neural network.Two subsets of spectrum data containing 36 samples each were acquired from tip position of tongues of healthy adults and viral hepatitis pa-tients respectively.Since being normalized,PCA was conducted to acquire principal components.Eight principal components（PCs）were selected based on accumulative reliabilities,and these selected PCs would be taken as the inputs of artificial neural network.Then 26 samples from each subset were selected randomly to make a modeling dataset utilized by a ANN,and the rest for testing samples.The result shows a 100% recognition correction under the condition that a predictive error threshold of ±0.2 is set.The experimental result shows that the PCA-BP method can achieve good classification and recognition on features of healthy people and viral hepatitis patients,which greatly promotes the objectiveness of traditional Chinese medicine tongue diagnosis.
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