Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set
Objective: The aim of this study is to compare the classification performances of hierarchical and non-hierarchical fuzzy models built by using different membership functions. Materials and Methods: In this study, normally distributed data sets containing different number of independent variables (p...
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| Veröffentlicht in: | Meandros medical and dental journal 2018-08, Vol.19 (2), p.138-146 |
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| creator | Cantaş, Fulden Kurt Ömürlü, İmran Türe, Mevlüt |
| description | Objective: The aim of this study is to compare the classification performances of
hierarchical and non-hierarchical fuzzy models built by using different membership
functions.
Materials and Methods: In this study, normally distributed data sets containing
different number of independent variables (p=3 and p=6) were generated. Besides,
the classification performances of hierarchical and non-hierarchical fuzzy models
built by using the data set which contained body mass index, fasting blood glucose
and triglyceride values of hypertensive (n=206) and control (n=113) people were
compared.
Results: It was found that there was a significant difference between the fuzzy
models (p |
| doi_str_mv | 10.4274/meandros.02996 |
| format | Article |
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hierarchical and non-hierarchical fuzzy models built by using different membership
functions.
Materials and Methods: In this study, normally distributed data sets containing
different number of independent variables (p=3 and p=6) were generated. Besides,
the classification performances of hierarchical and non-hierarchical fuzzy models
built by using the data set which contained body mass index, fasting blood glucose
and triglyceride values of hypertensive (n=206) and control (n=113) people were
compared.
Results: It was found that there was a significant difference between the fuzzy
models (p<0.001). According to the result of both simulation and hypertension
data set application, non-hierarchical fuzzy models were found to have better
classification performance than hierarchical fuzzy models according to sensitivity,
specificity, accuracy and root mean square criteria. Moreover, when number of
independent variables was increased, performances of the models increased too
and approached to each other.
Conclusion: In fuzzy logic methods, data structure, distributions of the variables
and correlation between them, how to divide independent variables into categories
and which of the fuzzy logic methods is to choose should be examined by taking
an expert support.</description><identifier>ISSN: 2149-9063</identifier><identifier>EISSN: 2149-9063</identifier><identifier>DOI: 10.4274/meandros.02996</identifier><language>eng</language><publisher>Aydın: Adnan Menderes Üniversitesi</publisher><subject>Algorithms ; Body mass index ; Classification ; Data processing ; Datasets ; Economic models ; Engineering research ; Fuzzy logic ; Gene expression ; Hypertension ; International conferences ; Mathematical models ; Neural networks ; Tıp ; Variables</subject><ispartof>Meandros medical and dental journal, 2018-08, Vol.19 (2), p.138-146</ispartof><rights>2018. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c269t-14e331237f5a8ac8df005fea51c85c78935464215b429d274ea5a9e432ca29563</citedby><orcidid>0000-0003-2887-6656</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Turan,Yasemin</contributor><creatorcontrib>Cantaş, Fulden</creatorcontrib><creatorcontrib>Kurt Ömürlü, İmran</creatorcontrib><creatorcontrib>Türe, Mevlüt</creatorcontrib><title>Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set</title><title>Meandros medical and dental journal</title><description>Objective: The aim of this study is to compare the classification performances of
hierarchical and non-hierarchical fuzzy models built by using different membership
functions.
Materials and Methods: In this study, normally distributed data sets containing
different number of independent variables (p=3 and p=6) were generated. Besides,
the classification performances of hierarchical and non-hierarchical fuzzy models
built by using the data set which contained body mass index, fasting blood glucose
and triglyceride values of hypertensive (n=206) and control (n=113) people were
compared.
Results: It was found that there was a significant difference between the fuzzy
models (p<0.001). According to the result of both simulation and hypertension
data set application, non-hierarchical fuzzy models were found to have better
classification performance than hierarchical fuzzy models according to sensitivity,
specificity, accuracy and root mean square criteria. Moreover, when number of
independent variables was increased, performances of the models increased too
and approached to each other.
Conclusion: In fuzzy logic methods, data structure, distributions of the variables
and correlation between them, how to divide independent variables into categories
and which of the fuzzy logic methods is to choose should be examined by taking
an expert support.</description><subject>Algorithms</subject><subject>Body mass index</subject><subject>Classification</subject><subject>Data processing</subject><subject>Datasets</subject><subject>Economic models</subject><subject>Engineering research</subject><subject>Fuzzy logic</subject><subject>Gene expression</subject><subject>Hypertension</subject><subject>International conferences</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Tıp</subject><subject>Variables</subject><issn>2149-9063</issn><issn>2149-9063</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpNkd1LwzAUxYsoOHSvPgd88Kkzn03zOPbhBlMfps8hpinLaJuadOj215tuioMLuTn53RO4J0nuEBxRzOljbVRTeBdGEAuRXSQDjKhIBczI5Vl_nQxD2EIIEc-YINkg-Z64ulXeBtcAV4KFNV55vbFaVSA6ghfXpJtzcb47HPbg2RWmCuDLdhuwtvWuUp2NDv2EasC4basIH6VYi31rfGea0N-nqlNgbbrb5KpUVTDD3_MmeZ_P3iaLdPX6tJyMV6nGmehSRA0hCBNeMpUrnRclhKw0iiGdM81zQRjNKEbsg2JRxE3EJyUMJVgrLFhGbpL7k2_r3efOhE5u3c438UuJEcoE45zTSD2cKFsYVbmmso35B5fT2XglOUei9xudSB23HbwpZettrfxeIij7KORfFPIYBfkBU2p-Qw</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Cantaş, Fulden</creator><creator>Kurt Ömürlü, İmran</creator><creator>Türe, Mevlüt</creator><general>Adnan Menderes Üniversitesi</general><general>Galenos Publishing House</general><scope>AAYXX</scope><scope>CITATION</scope><scope>IEBAR</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-2887-6656</orcidid></search><sort><creationdate>20180801</creationdate><title>Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set</title><author>Cantaş, Fulden ; Kurt Ömürlü, İmran ; Türe, Mevlüt</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c269t-14e331237f5a8ac8df005fea51c85c78935464215b429d274ea5a9e432ca29563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Body mass index</topic><topic>Classification</topic><topic>Data processing</topic><topic>Datasets</topic><topic>Economic models</topic><topic>Engineering research</topic><topic>Fuzzy logic</topic><topic>Gene expression</topic><topic>Hypertension</topic><topic>International conferences</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Tıp</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cantaş, Fulden</creatorcontrib><creatorcontrib>Kurt Ömürlü, İmran</creatorcontrib><creatorcontrib>Türe, Mevlüt</creatorcontrib><collection>CrossRef</collection><collection>Idealonline online kütüphane - Journals</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Meandros medical and dental journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cantaş, Fulden</au><au>Kurt Ömürlü, İmran</au><au>Türe, Mevlüt</au><au>Turan,Yasemin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set</atitle><jtitle>Meandros medical and dental journal</jtitle><date>2018-08-01</date><risdate>2018</risdate><volume>19</volume><issue>2</issue><spage>138</spage><epage>146</epage><pages>138-146</pages><issn>2149-9063</issn><eissn>2149-9063</eissn><abstract>Objective: The aim of this study is to compare the classification performances of
hierarchical and non-hierarchical fuzzy models built by using different membership
functions.
Materials and Methods: In this study, normally distributed data sets containing
different number of independent variables (p=3 and p=6) were generated. Besides,
the classification performances of hierarchical and non-hierarchical fuzzy models
built by using the data set which contained body mass index, fasting blood glucose
and triglyceride values of hypertensive (n=206) and control (n=113) people were
compared.
Results: It was found that there was a significant difference between the fuzzy
models (p<0.001). According to the result of both simulation and hypertension
data set application, non-hierarchical fuzzy models were found to have better
classification performance than hierarchical fuzzy models according to sensitivity,
specificity, accuracy and root mean square criteria. Moreover, when number of
independent variables was increased, performances of the models increased too
and approached to each other.
Conclusion: In fuzzy logic methods, data structure, distributions of the variables
and correlation between them, how to divide independent variables into categories
and which of the fuzzy logic methods is to choose should be examined by taking
an expert support.</abstract><cop>Aydın</cop><pub>Adnan Menderes Üniversitesi</pub><doi>10.4274/meandros.02996</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-2887-6656</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Body mass index Classification Data processing Datasets Economic models Engineering research Fuzzy logic Gene expression Hypertension International conferences Mathematical models Neural networks Tıp Variables |
| title | Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set |
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