title: Near infrared spectroscopy combined with least squares support vector machines and fuzzy rule-building expert system applied to diagnosis of endometrial carcinoma.
abstract: OBJECTIVE
  The feasibility of early diagnosis of endometrial carcinoma was studied by least squares support vector machines (LS-SVM) and fuzzy rule-building expert system (FuRES) that classified near infrared (NIR) spectra of tissues.
  
  
  METHODS
  NIR spectra of 77 specimens of endometrium were collected. The spectra were pretreated by principal component orthogonal signal correction (PC-OSC) and direct orthogonal signal correction (DOSC) methods to improve the signal-to-noise ratio (SNR) and remove the influences of background and baseline. The effects of modeling parameters were investigated using bootstrapped Latin-partition methods.
  
  
  RESULTS
  The optimal LS-SVM model of the PC-OSC pretreatment method successfully classified the samples with prediction accuracies of 96.8±1.4%.
  
  
  CONCLUSIONS
  The proposed procedure proved to be rapid and convenient, which is suitable to be developed as a non-invasive diagnosis method for cancer tissue.
authors.affiliation: Department of Chemistry, Capital Normal University, Beijing, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Yang
authors.forename: Jing
authors.initials: J
authors.lastname: Tian
authors.forename: Yuhong
authors.initials: Y
authors.lastname: Xiang
authors.forename: Zhuoyong
authors.initials: Z
authors.lastname: Zhang
authors.forename: Peter de B
authors.initials: Pde B
authors.lastname: Harrington
mesh_terms: D000328:Adult; D000368:Aged; D016889:Endometrial Neoplasms / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D016018:Least-Squares Analysis; D008875:Middle Aged; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D055815:Young Adult