A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan
According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In additio...
Gespeichert in:
Veröffentlicht in: | International journal of environmental research and public health 2018-12, Vol.16 (1), p.92 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 1 |
container_start_page | 92 |
container_title | International journal of environmental research and public health |
container_volume | 16 |
creator | Chen, Ming-Shu Chen, Shih-Hsin |
description | According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention. |
doi_str_mv | 10.3390/ijerph16010092 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6339104</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2328949068</sourcerecordid><originalsourceid>FETCH-LOGICAL-c418t-16556f03506f8dc8563900cb6544ca4ee9dcfe6537212d351e0f6174480110863</originalsourceid><addsrcrecordid>eNpdkb1PHDEQxa0oKBCSNmVkKU2ahfH647xNpNORQCQQRUht-byznE-79sX2gvjv4wBBQOWx5jdP8-YR8onBEecdHPstpt2GKWAAXfuGHDCloBH1__ZZvU_e57wF4Fqo7h3Z56CgVVIfELukJ7bY5iT5Gwx0mTPmPGEoNA60bJBeYLHrOHpHf92FPsUJ6Sr5gslbOsREl_08FnqGdiwbemGDvcb7cR_olfW3Nnwge4MdM358fA_J7x_fr1Znzfnl6c_V8rxxgunSMCWlGoBLUIPunZaq2gO3VlIIZwVi17sBleSLlrU9lwxhUGwhhAbGQCt-SL496O7m9YS9q0skO5pd8pNNdyZab152gt-Y63hjVD0kA1EFvj4KpPhnxlzM5LPDcbQB45xNyxQHJuWCVfTLK3Qb5xSqPdPyVneiA6UrdfRAuRRzTjg8LcPA_EvPvEyvDnx-buEJ_x8X_wthAJVm</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2328949068</pqid></control><display><type>article</type><title>A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan</title><source>MEDLINE</source><source>PubMed Central Open Access</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Chen, Ming-Shu ; Chen, Shih-Hsin</creator><creatorcontrib>Chen, Ming-Shu ; Chen, Shih-Hsin</creatorcontrib><description>According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph16010092</identifier><identifier>PMID: 30602658</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adult ; Adults ; Age Factors ; Aged ; Algorithms ; Blood ; Datasets ; Decision Trees ; Disease management ; Entropy ; Female ; Global health ; Health risks ; Humans ; Male ; Medical research ; Medical screening ; Metabolic disorders ; Metabolic syndrome ; Metabolic Syndrome - diagnosis ; Metabolic Syndrome - prevention & control ; Middle age ; Middle Aged ; Obesity ; Population Health Management ; Research methodology ; Researchers ; Risk Factors ; Studies ; Taiwan ; Womens health</subject><ispartof>International journal of environmental research and public health, 2018-12, Vol.16 (1), p.92</ispartof><rights>2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 by the authors. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-16556f03506f8dc8563900cb6544ca4ee9dcfe6537212d351e0f6174480110863</citedby><cites>FETCH-LOGICAL-c418t-16556f03506f8dc8563900cb6544ca4ee9dcfe6537212d351e0f6174480110863</cites><orcidid>0000-0002-2713-3546 ; 0000-0001-5635-536X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339104/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339104/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,886,27929,27930,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30602658$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Ming-Shu</creatorcontrib><creatorcontrib>Chen, Shih-Hsin</creatorcontrib><title>A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan</title><title>International journal of environmental research and public health</title><addtitle>Int J Environ Res Public Health</addtitle><description>According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention.</description><subject>Adult</subject><subject>Adults</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Blood</subject><subject>Datasets</subject><subject>Decision Trees</subject><subject>Disease management</subject><subject>Entropy</subject><subject>Female</subject><subject>Global health</subject><subject>Health risks</subject><subject>Humans</subject><subject>Male</subject><subject>Medical research</subject><subject>Medical screening</subject><subject>Metabolic disorders</subject><subject>Metabolic syndrome</subject><subject>Metabolic Syndrome - diagnosis</subject><subject>Metabolic Syndrome - prevention & control</subject><subject>Middle age</subject><subject>Middle Aged</subject><subject>Obesity</subject><subject>Population Health Management</subject><subject>Research methodology</subject><subject>Researchers</subject><subject>Risk Factors</subject><subject>Studies</subject><subject>Taiwan</subject><subject>Womens health</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkb1PHDEQxa0oKBCSNmVkKU2ahfH647xNpNORQCQQRUht-byznE-79sX2gvjv4wBBQOWx5jdP8-YR8onBEecdHPstpt2GKWAAXfuGHDCloBH1__ZZvU_e57wF4Fqo7h3Z56CgVVIfELukJ7bY5iT5Gwx0mTPmPGEoNA60bJBeYLHrOHpHf92FPsUJ6Sr5gslbOsREl_08FnqGdiwbemGDvcb7cR_olfW3Nnwge4MdM358fA_J7x_fr1Znzfnl6c_V8rxxgunSMCWlGoBLUIPunZaq2gO3VlIIZwVi17sBleSLlrU9lwxhUGwhhAbGQCt-SL496O7m9YS9q0skO5pd8pNNdyZab152gt-Y63hjVD0kA1EFvj4KpPhnxlzM5LPDcbQB45xNyxQHJuWCVfTLK3Qb5xSqPdPyVneiA6UrdfRAuRRzTjg8LcPA_EvPvEyvDnx-buEJ_x8X_wthAJVm</recordid><startdate>20181231</startdate><enddate>20181231</enddate><creator>Chen, Ming-Shu</creator><creator>Chen, Shih-Hsin</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2713-3546</orcidid><orcidid>https://orcid.org/0000-0001-5635-536X</orcidid></search><sort><creationdate>20181231</creationdate><title>A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan</title><author>Chen, Ming-Shu ; Chen, Shih-Hsin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-16556f03506f8dc8563900cb6544ca4ee9dcfe6537212d351e0f6174480110863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Adults</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Blood</topic><topic>Datasets</topic><topic>Decision Trees</topic><topic>Disease management</topic><topic>Entropy</topic><topic>Female</topic><topic>Global health</topic><topic>Health risks</topic><topic>Humans</topic><topic>Male</topic><topic>Medical research</topic><topic>Medical screening</topic><topic>Metabolic disorders</topic><topic>Metabolic syndrome</topic><topic>Metabolic Syndrome - diagnosis</topic><topic>Metabolic Syndrome - prevention & control</topic><topic>Middle age</topic><topic>Middle Aged</topic><topic>Obesity</topic><topic>Population Health Management</topic><topic>Research methodology</topic><topic>Researchers</topic><topic>Risk Factors</topic><topic>Studies</topic><topic>Taiwan</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Ming-Shu</creatorcontrib><creatorcontrib>Chen, Shih-Hsin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</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>ProQuest Central</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 Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Ming-Shu</au><au>Chen, Shih-Hsin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2018-12-31</date><risdate>2018</risdate><volume>16</volume><issue>1</issue><spage>92</spage><pages>92-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>30602658</pmid><doi>10.3390/ijerph16010092</doi><orcidid>https://orcid.org/0000-0002-2713-3546</orcidid><orcidid>https://orcid.org/0000-0001-5635-536X</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1660-4601 |
ispartof | International journal of environmental research and public health, 2018-12, Vol.16 (1), p.92 |
issn | 1660-4601 1661-7827 1660-4601 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6339104 |
source | MEDLINE; PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Adult Adults Age Factors Aged Algorithms Blood Datasets Decision Trees Disease management Entropy Female Global health Health risks Humans Male Medical research Medical screening Metabolic disorders Metabolic syndrome Metabolic Syndrome - diagnosis Metabolic Syndrome - prevention & control Middle age Middle Aged Obesity Population Health Management Research methodology Researchers Risk Factors Studies Taiwan Womens health |
title | A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-13T23%3A19%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Data-Driven%20Assessment%20of%20the%20Metabolic%20Syndrome%20Criteria%20for%20Adult%20Health%20Management%20in%20Taiwan&rft.jtitle=International%20journal%20of%20environmental%20research%20and%20public%20health&rft.au=Chen,%20Ming-Shu&rft.date=2018-12-31&rft.volume=16&rft.issue=1&rft.spage=92&rft.pages=92-&rft.issn=1660-4601&rft.eissn=1660-4601&rft_id=info:doi/10.3390/ijerph16010092&rft_dat=%3Cproquest_pubme%3E2328949068%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2328949068&rft_id=info:pmid/30602658&rfr_iscdi=true |