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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

An Artificial Neural Network Model for the Prediction of Bruxism by Means of Occlusal Variables

Authors : Ángel Álvarez-Arenal, Héctor deLlanos-Lanchares, Elena Martin-Fernandez, Carlos González-Gutiérrez, Mario Mauvezin-Quevedo, Francisco Javier de Cos Juez

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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Abstract

The objective of the present work was to create an artificial neural network model able to classify individuals suffering from bruxism in clenching and grinding patients according to the value of certain occlusal variables and other parameters. Patients suspected of bruxism represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may not need treatment at all.
Artificial neural network (ANN) ensembles models were trained on with data from 325 bruxist patients examined at the Department of Prosthodontics and Occlusion (Craniomandibular Dysfunction Unit) of Oviedo University. The information retrieved from each patient included some occlusal variables and other information such as their gender and age. The models were analysed using Receiver Operating Characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared between each model.
The ANN ensemble approach resulted in an area under the ROC curve of 86%. At 95% sensitivity the specificity was 84.1%, for the existence of 43.5% of bruxists clenching patients in the population of the study. This population corresponds to a grinding patients’ best predictive value of 97.2% and a clenching patients’ best predictive value of 89.5% both using the bagging method. The artificial neural network model obtained can distinguish between clenching and grinding patients requiring the analysis of a few variables and with a high rate of success.

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previous chapter Outcome Prediction for Salivary Gland Cancer Using Multivariate Adaptative Regression Splines (MARS) and Self-Organizing Maps (SOM)
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Metadata
Title
An Artificial Neural Network Model for the Prediction of Bruxism by Means of Occlusal Variables
Authors
Ángel Álvarez-Arenal
Héctor deLlanos-Lanchares
Elena Martin-Fernandez
Carlos González-Gutiérrez
Mario Mauvezin-Quevedo
Francisco Javier de Cos Juez
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_36

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