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DOI: 10.14569/IJACSA.2016.070517
PDF

The Factors of Subjective Voice Disorder Using Integrated Method of Decision Tree and Multi-Layer Perceptron Artificial Neural Network Algorithm

Author 1: Haewon Byeon
Author 2: Sunghyoun Cho

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 5, 2016.

  • Abstract and Keywords
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Abstract: The aim of the present study was to develop a prediction model for subjective voice disorders based on an artificial neural network algorithm and a decision tree using national statistical data. Subjects of analysis were 8,713 adults over the age of 19 (3,801 males and 4,912 females) who completed the otolaryngological examination of the Korea National Health and Nutrition Examination Survey from 2010 to 2012. Explanatory variables included age, education level, income, occupation, problem drinking, coffee consumption, and pain and discomfort from disease over the last two weeks. A multi-layer perceptron artificial neural network and a decision tree model were used for the analysis. In this model, smoking, pain and discomfort from disease over the last two weeks, education level, occupation, and income were drawn out as major predictors of subjective voice disorders. In order to minimize the risk of dysphonia, it is necessary to establish a scientific management system for high-risk groups.

Keywords: Neural Networks; Subjective Voice Disorder; decision tree; risk factor; data-mining

Haewon Byeon and Sunghyoun Cho, “The Factors of Subjective Voice Disorder Using Integrated Method of Decision Tree and Multi-Layer Perceptron Artificial Neural Network Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 7(5), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070517

@article{Byeon2016,
title = {The Factors of Subjective Voice Disorder Using Integrated Method of Decision Tree and Multi-Layer Perceptron Artificial Neural Network Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070517},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070517},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {5},
author = {Haewon Byeon and Sunghyoun Cho}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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