%0 Journal Article
%T Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks
%A Najla S Dar-Odeh
%A Othman M Alsmadi
%A Faris Bakri
%A et al
%J Advances and Applications in Bioinformatics and Chemistry
%D 2010
%I Dove Medical Press
%R http://dx.doi.org/10.2147/AABC.S10177
%X edicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks Original Research (4089) Total Article Views Authors: Najla S Dar-Odeh, Othman M Alsmadi, Faris Bakri, et al Published Date May 2010 Volume 2010:3 Pages 7 - 13 DOI: http://dx.doi.org/10.2147/AABC.S10177 Najla S Dar-Odeh1, Othman M Alsmadi2, Faris Bakri3, Zaer Abu-Hammour2, Asem A Shehabi3, Mahmoud K Al-Omiri1, Shatha M K Abu-Hammad4, Hamzeh Al-Mashni4, Mohammad B Saeed4, Wael Muqbil4, Osama A Abu-Hammad1 1Faculty of Dentistry, 2Faculty of Engineering and Technology, 3Faculty of Medicine, University of Jordan, Amman, Jordan; 4Dental Department, University of Jordan Hospital, Amman, Jordan Objective: To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data. Participants and methods: Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration) were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants. Results: The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin) and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits. Conclusions: Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.
%K artifical neural networks
%K recurrent
%K aphthous ulceration
%K ulcer
%U https://www.dovepress.com/predicting-recurrent-aphthous-ulceration-using-genetic-algorithms-opti-peer-reviewed-article-AABC