Multiscale Clustering and Profile Visualization of Malocclusion in Korean Orthodontic Patients: Cluster Analysis of Malocclusion
International Journal of Oral Biology
; : 101-111, 2018.
Article
in Ko
| WPRIM
| ID: wpr-740065
Responsible library:
WPRO
ABSTRACT
Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.
Key words
Full text:
1
Database:
WPRIM
Main subject:
Orthodontics
/
Passive Cutaneous Anaphylaxis
/
Cluster Analysis
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Cephalometry
/
Classification
/
Principal Component Analysis
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Malocclusion
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Mandible
/
Maxilla
Limits:
Female
/
Humans
/
Male
Language:
Ko
Journal:
International Journal of Oral Biology
Year:
2018
Document type:
Article