title: Patient classification of hypertension in Traditional Chinese Medicine using multi-label learning techniques.
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.
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
authors.forename: Zehui
authors.initials: Z
authors.lastname: He
authors.forename: Feng-Feng
authors.initials: FF
authors.lastname: Shao
authors.forename: Ai-Hua
authors.initials: AH
authors.lastname: Ou
authors.forename: Xiao-Zhong
authors.initials: XZ
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D057225:Data Mining; D006801:Humans; D006973:Hypertension / Q000175:diagnosis* / Q000473:pathology; D008516:Medicine, Chinese Traditional; D060388:Support Vector Machine; D013577:Syndrome