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An intelligent diagnosis method for Chronis hepatitis B in TCM

机译:中医慢性乙型肝炎的智能诊断方法

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In traditional Chinese medicine (TCM), it is frequently found that more than one syndrome of a patient are recognized in clinical practice, which has its own symptoms and signs. While, most algorithms are used to solve issues of syndrome diagnosis that only focus on one syndrome. Therefore, we proposed a hybrid intelligent syndrome diagnosis (HISD) model. Methods. The HTSD model combined feature selection methods to select the significant symptoms and signs corresponding to syndromes of CHB, and combined probability-classification methods to obtain the main syndrome and accompanying syndromes. The model was carried on 664 records of CHB. Results. 16 features were selected for the syndrome of Damp Heat in the Liver and Gallbladder (DHLG), 20 features were selected for the syndrome of Liver qi Stagnation and Spleen Deficiency (LSSD) and 13 features were selected for the syndrome of Yin Deficiency of Liver and Kidney (YDLK). The lowest average accuracy was 80.52% using logitboost, whereas the accuracy of HISD was 85% for unrecognized cases of CHB. Conclusion. Our method extracts the relevant symptoms and signs for each syndrome, recognizes the main syndrome and accompanying syndromes, and improves its recognition accuracy.
机译:在中医(TCM)中,经常发现在临床实践中认识到不止一种患者的综合征,这具有其自身的症状和体征。同时,大多数算法用于解决仅针对一种综合症的综合症诊断问题。因此,我们提出了一种混合智能综合症诊断(HISD)模型。方法。 HTSD模型结合特征选择方法来选择与CHB证候相对应的重要症状和体征,并结合概率分类方法来获得主要证候和伴随证候。该模型进行了664条CHB记录。结果。选择肝胆湿热证(DHLG)16个,肝郁脾虚证(LSSD)20个,肝肾阴虚证13个。肾脏(YDLK)。使用logitboost的最低平均准确度为80.52%,而对于无法识别的CHB病例,HISD的准确度为85%。结论。我们的方法提取每种综合症的相关症状和体征,识别主要综合症和伴随综合症,并提高其识别准确性。

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