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Prognosis Prediction for Class III Malocclusion Treatment by Feature Wrapping Method

Cited 5 time in Web of Science Cited 5 time in Scopus
Authors
Baek, Seung-Hak; Kim, Bo-Mi; Kang, Bo-Yeong; Kim, Hong-Gee
Issue Date
2009-07
Publisher
Angle Orthodontist / E.H Angle Education and Research Foundation
Citation
Angle Orthodontist. 2009;79:683-691
Keywords
Class III malocclusionPrognosis predictionFeature wrapping methodSequential forward searchalgorithmSupport vector machine
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).

Materials and 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 (°) 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.
ISSN
0003-3219
Language
English
URI
http://hdl.handle.net/10371/68663
DOI
https://doi.org/10.2319/071508-371.1
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College of Dentistry/School of Dentistry (치과대학/치의학대학원)Dept. of Dentistry (치의학과)Journal Papers (저널논문_치의학과)
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