Dental informatics to characterize patients with dentofacial deformities.
Relevant statistical modeling and analysis of dental data can improve diagnostic and treatment procedures. The purpose of this study is to demonstrate the use of various data mining algorithms to characterize patients with dentofacial deformities. A total of 72 patients with skeletal malocclusions who had completed orthodontic and orthognathic surgical treatments were examined. Each patient was characterized by 22 measurements related to dentofacial deformities. Clustering analysis and visualization grouped the patients into three different patterns of dentofacial deformities. A feature selection approach based on a false discovery rate was used to identify a subset of 22 measurements important in categorizing these three clusters. Finally, classification was performed to evaluate the quality of the measurements selected by the feature selection approach. The results showed that feature selection improved classification accuracy while simultaneously determining which measurements were relevant.
School of Industrial Management Engineering, Korea University, Anam-Dong, Seongbuk-Gu, Seoul, Republic of Korea.
Seoung Bum
SB
Kim
Jung Woo
JW
Lee
Sin Young
SY
Kim
Deok Won
DW
Lee
D000465:Algorithms; D016000:Cluster Analysis; D048048:Dental Informatics; D063169:Dentofacial Deformities / Q000145:classification* / Q000175:diagnosis*; D006801:Humans