Medidas radiográficas y corporales para diagnósticar relaciones de clase ll utilizando análisis estadístico multivariado

ABSTRACT: More than 40 variables are used in the process of orthodontic diagnosis and each one of them depends on every individual author. Of these variables only some contribute sufficient information to carry out a diagnosis of class II. When so many variables are available it is necessary to use...

Full description

Autores:
Rincón Rodríguez, Ramiro Javier
Restrepo R., Ligia Isabel
Agudelo González, Yakeline
Echeverri V., Juan David
Tipo de recurso:
Article of investigation
Fecha de publicación:
2003
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
spa
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/8751
Acceso en línea:
http://hdl.handle.net/10495/8751
Palabra clave:
Diagnóstico
Maloclusión clase II de Angle
Análisis multivariante
Análisis de cluster
Análisis de componentes principales
Análisis de factor
Análisis discriminante
Diagnosis
Class II malocclusion
Multivariate analysis
Claster analysis
Main component analysis
Factor analysis
Discriminant analysis
Rights
openAccess
License
Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO)
Description
Summary:ABSTRACT: More than 40 variables are used in the process of orthodontic diagnosis and each one of them depends on every individual author. Of these variables only some contribute sufficient information to carry out a diagnosis of class II. When so many variables are available it is necessary to use a method that allows to identify the representative variables and to eliminate those that are redundant. To obtain this objective, a multiway analysis of variance was used (Main Components Analysis, Factor Analysis, Cluster Analysis and Discriminate Analysis), which identifies the variables of Angle of Lande, the Angle of Convexity and the distance from the point A to the perpendicular of FH, the subgroups and the factors were identified (constructed) by gender and lastly the Discriminate Function was obtained to identify the Class II malocclusion. With the Multiway Analysis of variance it was possible to reduce the number of variables used for the diagnostic process.