Understanding interactions among cephalometrics variables during growth in untreated Class III subjects

Objective: The aim of the present study was to apply a computational method commonly used in data mining discipline, classification trees (CTs), to evaluate the growth features in untreated Class III subjects. Materials and methods: CT was applied to data from 91 untreated Class III subjects (48 females and 43 males) and compared with the results of discriminant analysis (DA). For all subjects, lateral cephalograms were available at T1 (mean age 10.4 +/- 2.0 years) and at T2 (mean age 15.4 +/- 1.9 years). A cephalometric analysis comprising 11 variables was performed. The subjects were divided into two subgroups, unfavourable ('Bad') and favourable ('Good') growers, according to the quality of the skeletal growth rate in comparison with the normal craniofacial growth. Results: CTs showed that the most informative attribute for the prediction of favourable/unfavourable skeletal growth was the SNA angle. Subjects with SNA values lower than 79.1 degrees showed a risk of 94 per cent of growing unfavourably. DA was able to select palatal plane to mandibular plane angle as predictors. DA, however, showed a statistically significant higher rate of misclassification when compared with CTs (40.7 per cent versus 12.1 per cent, binomial exact test: odds ratio = 6.20; P < 0.0001). Conclusions: CTs provided a valid measure of elucidating the effective contribution of craniofacial characteristics in predicting favourable/unfavourable growth in untreated Class III subjects.