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<ArticleSet>
<Article>
<Journal>
<PublisherName>Iranian Association of Medical Informatics</PublisherName>
<JournalTitle>Frontiers in Health Informatics</JournalTitle>
<Issn>2676-7104</Issn>
<Volume>13</Volume>
<Issue>0</Issue>
<PubDate>
<Year>2024</Year>
<Month>04</Month>
<Day>20</Day>
</PubDate>
</Journal>
<ArticleTitle>Voice as an Indicator for Laryngeal Disorders Using Data Mining Approach</ArticleTitle>
<FirstPage>605</FirstPage>
<LastPage>605</LastPage>
<ELocationID>10.30699/fhi.v13i0.605</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Mohammadjavad</FirstName>
<LastName>Sayadi</LastName>
<Affiliation>. sayadi.javad@gmail.com</Affiliation>
</Author>
<Author>
<FirstName>Mostafa</FirstName>
<LastName>Langarizadeh</LastName>
</Author>
<Author>
<FirstName>Farhad</FirstName>
<LastName>Torabinezhad</LastName>
</Author>
<Author>
<FirstName>Gholamreza</FirstName>
<LastName>Bayazian</LastName>
</Author>
</AuthorList>
<History>
<PubDate>
<Year>2024</Year>
<Month>02</Month>
<Day>28</Day>
</PubDate>
<PubDate>
<Year>2024</Year>
<Month>04</Month>
<Day>07</Day>
</PubDate>
</History>
<Abstract>Introduction: Laryngeal disorders are a common health problem that affects people of all ages, genders, and races. One of the main symptoms of laryngeal disorders is changes in the voice, which can be used as an indicator for the presence of such disorders. In this paper, we present a data mining approach for using voice as an indicator for laryngeal disorders.Material and Methods: We collected a dataset of voice recordings from individuals with and without laryngeal disorders including 434 people from two clinical centers in Tehran. The dataset was created using a powerful signal processing program and then based on the difference between male and female voice, the dataset was separated into two datasets. Finally, a Deep Neural Network was implemented for modelling using Python programming language and F1-score, Accuracy, Sensitivity, Specificity, and AUC as the model’s evaluation metrics were reported.Results: Among all the acoustic features, 23 features were selected for the male dataset and 25 features for the female data set. For the male dataset the final model achieved F1-Score of 0.915 and Accuracy of 0.910. For the female dataset the result was 0.884 of F1-Score and 0.896 of Accuracy.Conclusion: Our results show that machine learning algorithms can accurately classify voice recordings into two groups: individuals with laryngeal disorders and those without. The high accuracy achieved by the algorithms suggests that voice can be used as an objective and automated diagnostic tool for laryngeal disorders.</Abstract>
<ObjectList>
<Object>
<Param>Voice</Param>
</Object>
<Object>
<Param>Laryngeal Disorders</Param>
</Object>
<Object>
<Param>Indicator</Param>
</Object>
<Object>
<Param>Data Mining</Param>
</Object>
<Object>
<Param></Param>
</Object>
</ObjectList>
</Article>
</ArticleSet>

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