粗糙集结合神经网络的哮喘辨证分型法

Asthma Syndrome Differentiation Method of Rough Set Combined with Neural Network

传统中医辨证模式都是依靠个人经验进行观察判断和分析,主观性强,利用智能信息处理方法,能使中医诊断中的辩证得到客观量化。利用粗糙集理论对数据的属性进行约简,从而简化决策表,寻求症候辩证的更优解,以达到更高的准确率。将粗糙集与BP神经网络相结合对哮喘进行辨证分型,收敛效果和准确率方面都有较大提高,实验结果表明该融合方法对训练速度有很大提高,准确率可以达到90%,效果大大好于使用BP神经网络单分类器。

Syndrome differentiation of Traditional Chinese Medicine（TCM） rely on personal experience to observe, judge and analysis, such behavior has strong subjectivity. Using the method of intelligent information processing can make the quantization of syndrome differentiation in TCM diagnosis objectively. Using the theory of rough set to reduce the prop- erty of data, so as to simplify the decision table. Finding the more optimal solution for symptoms dialectical, in order to a- chieve a higher accuracy. The convergence effect and accuracy of asthma syndrome differentiation method of rough set com- bined with BP neural network has improved greatly. Experimental results show that the fusion method can improve the train- ing speed a lot and its accuracy can reach 90%, the effect is greatly better than that of using single BP neural network classifier.