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

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 t...

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Vydané v:International journal of advanced computer science & applications Ročník 7; číslo 5
Hlavní autori: Byeon, Haewon, Cho, Sunghyoun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2016
Predmet:
Algorithms
Artificial neural networks
Decision analysis
Decision trees
Discomfort
Disorders
Education
Income
Multilayer perceptrons
Multilayers
Neural networks
Pain
Prediction models
Voice
ISSN:2158-107X, 2156-5570
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2016.070517