title: Prediction of Class III treatment outcomes through orthodontic data mining.
abstract: OBJECTIVE
  To determine whether it is possible to predict Class III treatment outcomes on the basis of a model derived from a combination of computational analyses derived from complexity science, such as fuzzy clustering repartition and network analysis.
  
  
  METHODS
  Cephalometric data of 54 Class III patients (32 females, 22 males) taken before (T1, mean age 8.2 ± 1.6 years) and after (T2, mean age 14.6 ± 1.8 years) early rapid maxillary expansion and facemask therapy followed by fixed appliances were analysed. Patients were classified at T1 on the basis of high membership grade into three main dentoskeletal fuzzy cluster phenotypes: hyperdivergent (HD), hypermandibular (HM), and balanced (Bal) phenotypes. The prevalence rate of successful and unsuccessful cases at T2 was calculated for the three clusters and compared by means of Fisher's exact test corrected for multiple testing (Holm-Bonferroni method).
  
  
  RESULTS
  Unsuccessful cases were 9 out of 54 patients (16.7%). Once patients were framed into their cluster membership, the individualized pre-treatment prediction of unsuccessful cases was largely differentiated: HD and HM patients showed a significantly greater prevalence rate of unsuccessful cases than Bal patients (0% in Bal cluster, 28.6% in HM cluster, and 33.3% in HD cluster). Network analysis captured some noticeable interdependencies of Class III patients, showing a more connected interactive structure of cephalometric data sets in HM and HD patients compared with Bal patients. The results were confirmed after minimizing the geometrical connections between cephalometric variables in the model.
  
  
  CONCLUSIONS
  Fuzzy clustering repartition can be usefully used to estimate an individualized risk of unsuccessful treatment outcome in Class III patients.
authors.affiliation: *Private practice of Orthodontics, Rome, Italy.
authors.forename: Pietro
authors.initials: P
authors.lastname: Auconi
authors.affiliation: **Freelance Orthodontic Data Analyist, Rome, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Scazzocchio
authors.affiliation: ***Department of Orthodontics, University of Rome Tor Vergata, Rome, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Cozza
authors.affiliation: ****Department of Orthodontics and Pediatric Dentistry, School of Dentistry, *****Center for Human Growth and Development, University of Michigan, Ann Arbor, USA, and.
authors.forename: James A
authors.initials: JA
authors.lastname: McNamara
authors.affiliation: ******Department of Surgery and Translational Medicine, Orthodontics, Università degli Studi di Firenze, Florence, Italy lorenzo.franchi@unifi.it.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Franchi
mesh_terms: D002508:Cephalometry / Q000706:statistics & numerical data; D002648:Child; D016000:Cluster Analysis; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D057225:Data Mining / Q000706:statistics & numerical data*; D005115:Extraoral Traction Appliances; D005260:Female; D005544:Forecasting; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008313:Malocclusion, Angle Class III / Q000628:therapy*; D016571:Neural Networks, Computer; D010155:Palatal Expansion Technique / Q000295:instrumentation; D066230:Patient-Specific Modeling; D010641:Phenotype; D016896:Treatment Outcome