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Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients.

Authors :
Barelli E
Ottaviani E
Auconi P
Caldarelli G
Giuntini V
McNamara JA Jr
Franchi L
Source :
Scientific reports [Sci Rep] 2019 Apr 17; Vol. 9 (1), pp. 6189. Date of Electronic Publication: 2019 Apr 17.
Publication Year :
2019

Abstract

The aim of the study was to investigate how to improve the forecasting of craniofacial unbalance risk during growth among patients affected by Class III malocclusion. To this purpose we used computational methodologies such as Transductive Learning (TL), Boosting (B), and Feature Engineering (FE) instead of the traditional statistical analysis based on Classification trees and logistic models. Such techniques have been applied to cephalometric data from 728 cross-sectional untreated Class III subjects (6-14 years of age) and from 91 untreated Class III subjects followed longitudinally during the growth process. A cephalometric analysis comprising 11 variables has also been performed. The subjects followed longitudinally were divided into two subgroups: favourable and unfavourable growth, in comparison with normal craniofacial growth. With respect to traditional statistical predictive analytics, TL increased the accuracy in identifying subjects at risk of unfavourable growth. TL algorithm was useful in diffusion of information from longitudinal to cross-sectional subjects. The accuracy in identifying high-risk subjects to growth worsening increased from 63% to 78%. Finally, a further increase in identification accuracy, up to 83%, was produced by FE. A ranking of important variables in identifying subjects at risk of growth worsening, therefore, has been obtained.

Details

Language :
English
ISSN :
2045-2322
Volume :
9
Issue :
1
Database :
MEDLINE
Journal :
Scientific reports
Publication Type :
Academic Journal
Accession number :
30996304
Full Text :
https://doi.org/10.1038/s41598-019-42384-7