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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 106236, 7 pages
http://dx.doi.org/10.1155/2014/106236
Research Article

Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia

Received 13 February 2014; Revised 16 June 2014; Accepted 16 June 2014; Published 10 July 2014

Academic Editor: Qizhai Li

Copyright © 2014 Ali Al Haidan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.