<?xml version="1.0" encoding="utf-8"?>
<journal>
<title>Iranian Biomedical Journal</title>
<title_fa>مجله بیومدیکال ایران</title_fa>
<short_title>IBJ</short_title>
<subject>Basic Sciences</subject>
<web_url>http://ibj.pasteur.ac.ir</web_url>
<journal_hbi_system_id>1</journal_hbi_system_id>
<journal_hbi_system_user>admin</journal_hbi_system_user>
<journal_id_issn>1028-852X</journal_id_issn>
<journal_id_issn_online>2008-823X</journal_id_issn_online>
<journal_id_pii>-</journal_id_pii>
<journal_id_doi>10.61882/ibj</journal_id_doi>
<journal_id_iranmedex></journal_id_iranmedex>
<journal_id_magiran></journal_id_magiran>
<journal_id_sid>-</journal_id_sid>
<journal_id_nlai>8888</journal_id_nlai>
<journal_id_science>-</journal_id_science>
<language>en</language>
<pubdate>
	<type>jalali</type>
	<year>1397</year>
	<month>6</month>
	<day>1</day>
</pubdate>
<pubdate>
	<type>gregorian</type>
	<year>2018</year>
	<month>9</month>
	<day>1</day>
</pubdate>
<volume>22</volume>
<number>5</number>
<publish_type>online</publish_type>
<publish_edition>1</publish_edition>
<article_type>fulltext</article_type>
<articleset>
	<article>


	<language>en</language>
	<article_id_doi></article_id_doi>
	<title_fa></title_fa>
	<title>Comparing Three Data Mining Algorithms for Identifying 
the Associated Risk Factors of Type 2 Diabetes</title>
	<subject_fa>Enzymology and Protein Chemistry</subject_fa>
	<subject>Enzymology and Protein Chemistry</subject>
	<content_type_fa>مقاله کامل</content_type_fa>
	<content_type>Full Length/Original Article</content_type>
	<abstract_fa></abstract_fa>
	<abstract>&lt;strong&gt;Background&lt;/strong&gt;: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and health administrators. The current study aimed at developing and comparing some statistical models to identify the risk factors associated with type 2 diabetes. In this light, artificial neural network (ANN), support vector machines (SVMs), and multiple logistic regression (MLR) models were applied, using demographic, anthropometric, and biochemical characteristics, on a sample of 9528 individuals from Mashhad City in Iran. &lt;strong&gt;Methods&lt;/strong&gt;: This study has randomly selected 6654 (70%) cases for training and reserved the remaining 2874 (30%) cases for testing. The three methods were compared with the help of ROC curve. &lt;strong&gt;Results&lt;/strong&gt;: The prevalence rate of type 2 diabetes was 14% in our population. The ANN model had 78.7% accuracy, 63.1% sensitivity, and 81.2% specificity. Also, the values of these three parameters were 76.8%, 64.5%, and 78.9%, for SVM and 77.7%, 60.1%, and 80.5% for MLR. The area under the ROC curve was 0.71 for ANN, 0.73 for SVM, and 0.70 for MLR. &lt;strong&gt;Conclusion&lt;/strong&gt;: Our findings showed that ANN performs better than the two models (SVM and MLR) and can be used effectively to identify the associated risk factors of type 2 diabetes.</abstract>
	<keyword_fa></keyword_fa>
	<keyword>Support vector machine, Data mining, Diabetes type 2</keyword>
	<start_page>303</start_page>
	<end_page>311</end_page>
	<web_url>http://ibj.pasteur.ac.ir/browse.php?a_code=A-10-1-711&amp;slc_lang=en&amp;sid=1</web_url>


<author_list>
	<author>
	<first_name>Habibollah</first_name>
	<middle_name></middle_name>
	<last_name>Esmaeily</last_name>
	<suffix></suffix>
	<first_name_fa></first_name_fa>
	<middle_name_fa></middle_name_fa>
	<last_name_fa></last_name_fa>
	<suffix_fa></suffix_fa>
	<email></email>
	<code></code>
	<orcid>0000-0003-4139-546X</orcid>
	<coreauthor>Yes
</coreauthor>
	<affiliation>Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran</affiliation>
	<affiliation_fa></affiliation_fa>
	 </author>


	<author>
	<first_name>Maryam</first_name>
	<middle_name></middle_name>
	<last_name>Tayefi</last_name>
	<suffix></suffix>
	<first_name_fa></first_name_fa>
	<middle_name_fa></middle_name_fa>
	<last_name_fa></last_name_fa>
	<suffix_fa></suffix_fa>
	<email></email>
	<code></code>
	<orcid>0000-0003-4637-7754</orcid>
	<coreauthor>No</coreauthor>
	<affiliation>Department of Modern Sciences and Technologies, School of Medicine, Mashhad University of  Medical Sciences, Mashhad, Iran</affiliation>
	<affiliation_fa></affiliation_fa>
	 </author>


	<author>
	<first_name>Majid  </first_name>
	<middle_name></middle_name>
	<last_name>Ghayour-Mobarhan</last_name>
	<suffix></suffix>
	<first_name_fa></first_name_fa>
	<middle_name_fa></middle_name_fa>
	<last_name_fa></last_name_fa>
	<suffix_fa></suffix_fa>
	<email></email>
	<code></code>
	<orcid>0000-0002-1081-6754</orcid>
	<coreauthor>No</coreauthor>
	<affiliation>Biochemistry of Nutrition Research Center, School of Medicine,  Mashhad University of Medical Sciences, Mashhad, Iran</affiliation>
	<affiliation_fa></affiliation_fa>
	 </author>


	<author>
	<first_name>Alireza</first_name>
	<middle_name></middle_name>
	<last_name>Amirabadizadeh</last_name>
	<suffix></suffix>
	<first_name_fa></first_name_fa>
	<middle_name_fa></middle_name_fa>
	<last_name_fa></last_name_fa>
	<suffix_fa></suffix_fa>
	<email></email>
	<code></code>
	<orcid>0000-0002-2495-5042</orcid>
	<coreauthor>No</coreauthor>
	<affiliation>Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, South Khorasan, Iran</affiliation>
	<affiliation_fa></affiliation_fa>
	 </author>


</author_list>


	</article>
</articleset>
</journal>
