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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Álvarez-Arenal, Á., deLlanos-Lanchares, H., Martin-Fernandez, E., González-Gutiérrez, C., Mauvezin-Quevedo, M., de Cos Juez, F.J. (2018). An Artificial Neural Network Model for the Prediction of Bruxism by Means of Occlusal Variables. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_36
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