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.