Original article Prediction of Class III treatment outcomes through orthodontic data mining

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). Networ