title: Prognosis prediction for Class III malocclusion treatment by feature wrapping method.
abstract: OBJECTIVE
  To use the feature wrapping (FW) method to identify which cephalometric markers show the highest classification accuracy in prognosis prediction for Class III malocclusion and to compare the prediction accuracy between the FW method and conventional statistical methods such as discriminant analysis (DA).
  
  
  METHODS
  The sample set consisted of 38 patients (15 boys and 23 girls, mean age 8.53 +/- 1.36 years) who were diagnosed with Class III malocclusion and received both first-phase (orthopedic) and second-phase (fixed orthodontic) treatments. Lateral cephalograms were taken before (T0) and after first-phase treatment (T1) and after second-phase treatment and retention (T2). Based on the measurements taken at the T2 stage, the patients were allocated into good (n = 20) or poor (n = 18) prognosis groups. Forty-six cephalometric variables on T0 lateral cephalograms were analyzed by the FW method to identify key determinants for discriminating between the two groups. Sequential forward search (SFS) algorism and support vector machine (SVM) were used in conjunction with the FW method to improve classification accuracy. To compare the prediction accuracy of the FW method with conventional statistical methods, DA was performed for the same data set.
  
  
  RESULTS
  AB to mandibular plane angle ( degrees ) and A to N-perpendicular (mm) were selected as the most accurate cephalometric predictors by both the FW and DA methods. However, classification accuracy was higher with the FW method (97.2%) compared with DA (92.1%), because the FW method with SFS and SVM has a more precise classification algorithm.
  
  
  CONCLUSIONS
  The FW method, which uses a learning algorithm, might be an effective alternative to DA for prognosis prediction.
authors.affiliation: Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, South Korea.
authors.forename: Bo-Mi
authors.initials: BM
authors.lastname: Kim
authors.forename: Bo-Yeong
authors.initials: BY
authors.lastname: Kang
authors.forename: Hong-Gee
authors.initials: HG
authors.lastname: Kim
authors.forename: Seung-Hak
authors.initials: SH
authors.lastname: Baek
mesh_terms: D000465:Algorithms; D002508:Cephalometry / Q000145:classification*; D002648:Child; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D005260:Female; D005544:Forecasting / Q000379:methods; D006801:Humans; D008297:Male; D008313:Malocclusion, Angle Class III / Q000628:therapy*; D017063:Outcome Assessment, Health Care / Q000379:methods*; D025341:Principal Component Analysis; D011379:Prognosis