Feature Selection Based on Random Forest and Application in Correlation Analysis of Symptom and Disease
A set of simple, rational diagnosis mode is the effective premise for intelligent diagnosis model. In this paper, selected the important symptoms of five endogenous pathogens (FEP) and measured these symptoms contribution degree to FEP were main contents of this paper. Focused on the disease characteristics of FEP, we introduced the method of random forest (RF), and used it to build feature selection evaluation criteria, then proved the effectiveness of this method. On this basis, the article also explored the effective way to build an intelligent diagnosis model for FEP. Comparative experiment shown that RF model was superior in the diagnosis performance than the multi-classification support vector machine (SVM) classifier, and proven it to be an effective and high-performance FEP diagnosis model.
HU Xue-qin CUI Meng CHEN Bing
Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Department of Cognitive Science, Xiamen University, Xiamen, 361005, China
国际会议
2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)
济南
英文
120-124
2009-08-14（万方平台首次上网日期，不代表论文的发表时间）