Abstract
BACKGROUND: Hypertension is one of the major risk factors for cardiovascular diseases. Research on the patient classification of hypertension has become an important topic because Traditional Chinese Medicine lies primarily in "treatment based on syndromes differentiation of the patients". METHODS: Clinical data of hypertension was collected with 12 syndromes and 129 symptoms including inspection, tongue, inquiry, and palpation symptoms. Syndromes differentiation was modeled as a patient classification problem in the field of data mining, and a new multi-label learning model BrSmoteSvm was built dealing with the class-imbalanced of the dataset. RESULTS: The experiments showed that the BrSmoteSvm had a better results comparing to other multi-label classifiers in the evaluation criteria of Average precision, Coverage, One-error, Ranking loss. CONCLUSIONS: BrSmoteSvm can model the hypertension's syndromes differentiation better considering the imbalanced problem.