To build a recognition mode of phlegm-heat stasis syndrome according to indexes of clinical routine examinations of patients with unstable angina (UA).Methods:From April 2010 to April 2011,the clinical basic data,TCM four-diagnosis information and clinical routine examination indexes of 411 patients with UA were collected and normalized,and the recognition rules of phlegm-heat stasis syndrome was automatically extracted from 90 clinical examination indexes by applying CHAID decision tree method.The verification of recognition mode of phlegm-heat stasis syndrome in 212 patients was conducted.Results:A total of 8 indicators,including Cl ion,shortening fraction,RDW-CV,blood RBC,D-II mer,CK-MB,PTA and BUN were indexed into the decision tree model.The test results of 411 patients of the model showed that the sensitivity was 75.0%,the specificity was 86.9%,and the correct rate was 86.1%.The verification of association patterns lacked RDW-CV,and its correct rate was 85.8%.The sensitivity was 89.5%,and the specificity was 85.5%.Conclusion:The CHAID decision tree model can clearly and directly conduct recognition of phlegm-heat stasis syndrome with the basis of clinical routine examination indexes,and automatically summarize recognition rules,which has certain advantages in data mining of syndrome-physiochemistry model.