A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk
It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives...
Gespeichert in:
| Veröffentlicht in: | PloS one 2014-01, Vol.9 (1), p.e84897 |
|---|---|
| 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 | e84897 |
| container_title | PloS one |
| container_volume | 9 |
| creator | Lee, Bum Ju Kim, Jong Yeol |
| description | It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.
For 12789 subjects 21-85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.
The best indicator for predicting hypertension was rib circumference in both women (p = |
| doi_str_mv | 10.1371/journal.pone.0084897 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1491323684</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A478851101</galeid><doaj_id>oai_doaj_org_article_2122dca8dc7c481eab738c5de8e18ca3</doaj_id><sourcerecordid>A478851101</sourcerecordid><originalsourceid>FETCH-LOGICAL-c721t-ceedd144d58c0134fb5d095b4c309607db20311f0cff1cd455663b2e218452913</originalsourceid><addsrcrecordid>eNqNkl2L1DAUhoso7rr6D0QLguDFjPlqm94Iw-LHwMKCX7chTU6nGdumm6Sr--9Nnc4yBQXJRU7Oec6b5PAmyXOM1pgW-O3ejq6X7XqwPawR4oyXxYPkHJeUrHKC6MOT-Cx54v0eoYzyPH-cnBHG8oyx8jzRm1TZbpDOeNuntk5DA-ngQBsVzG0M7U9wU172oXF2sB0EZ1Rq-kiAT2vr0uZuABeg9yZKyF5PCXs8R-EfT5NHtWw9PJv3i-Tbh_dfLz-trq4_bi83VytVEBxWCkBrzJjOuEKYsrrKNCqziimKyhwVuopfwbhGqq6x0izL8pxWBAjmLCMlphfJy4Pu0Fov5gF5gVmsEZpzFontgdBW7sXgTCfdnbDSiD8J63ZCumBUC4JgQrSSXKtCMY5BVgXlKtPAAXMladR6N982Vh1oBX1wsl2ILiu9acTO3gpaIsRQHgVezQLO3ozgwz-ePFM7GV9l-tpGMdUZr8SGFZxnGKPp6-u_UHFp6IyKFqlNzC8a3iwaIhPgV9jJ0Xux_fL5_9nr70v29QnbgGxD4207hmgGvwTZAVTOeu-gvp8cRmJy-HEaYnK4mB0e216cTv2-6Whp-hv5Mvee</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1491323684</pqid></control><display><type>article</type><title>A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Lee, Bum Ju ; Kim, Jong Yeol</creator><creatorcontrib>Lee, Bum Ju ; Kim, Jong Yeol</creatorcontrib><description>It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.
For 12789 subjects 21-85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.
The best indicator for predicting hypertension was rib circumference in both women (p = <0.0001; OR = 1.813; AUC = 0.669) and men (p = <0.0001; OR = 1.601; AUC = 0.627); for hypotension, the strongest predictor was chest circumference in women (p = <0.0001; OR = 0.541; AUC = 0.657) and neck circumference in men (p = <0.0001; OR = 0.522; AUC = 0.672). In experiments using combined indices, the areas under the receiver operating characteristic curves (AUC) for the prediction of hypertension risk in women and men were 0.721 and 0.652, respectively, according to the logistic regression with wrapper-based variable selection; for hypotension, the corresponding values were 0.675 in women and 0.737 in men, according to the naïve Bayes with wrapper-based variable selection.
The best indicators of the risk of hypertension and the risk of hypotension may differ. The use of combined indices seems to slightly improve the predictive power for both hypertension and hypotension.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0084897</identifier><identifier>PMID: 24465449</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Algorithms ; Anthropometry ; Area Under Curve ; Bayesian analysis ; Blood pressure ; Body fat ; Body Mass Index ; Body measurements ; Body Size ; Body Weight ; Cardiology ; Cardiovascular disease ; Circumferences ; Comparative analysis ; Data mining ; Data processing ; Dementia ; Engineering ; Female ; Health risk assessment ; Humans ; Hypertension ; Hypertension - pathology ; Hypotension ; Hypotension - pathology ; Indicators ; Learning algorithms ; Machine learning ; Male ; Mathematics ; Medical research ; Medicine ; Men ; Mens health ; Metabolic syndrome ; Middle Aged ; Neck ; Obesity ; Predictions ; Regression analysis ; Risk ; Risk factors ; ROC Curve ; Social and Behavioral Sciences ; Statistical analysis ; Statistical analysis of data ; Studies ; Type 2 diabetes ; Womens health ; Young Adult</subject><ispartof>PloS one, 2014-01, Vol.9 (1), p.e84897</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Lee, Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2014 Lee, Kim 2014 Lee, Kim</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c721t-ceedd144d58c0134fb5d095b4c309607db20311f0cff1cd455663b2e218452913</citedby><cites>FETCH-LOGICAL-c721t-ceedd144d58c0134fb5d095b4c309607db20311f0cff1cd455663b2e218452913</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900406/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900406/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24465449$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Bum Ju</creatorcontrib><creatorcontrib>Kim, Jong Yeol</creatorcontrib><title>A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.
For 12789 subjects 21-85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.
The best indicator for predicting hypertension was rib circumference in both women (p = <0.0001; OR = 1.813; AUC = 0.669) and men (p = <0.0001; OR = 1.601; AUC = 0.627); for hypotension, the strongest predictor was chest circumference in women (p = <0.0001; OR = 0.541; AUC = 0.657) and neck circumference in men (p = <0.0001; OR = 0.522; AUC = 0.672). In experiments using combined indices, the areas under the receiver operating characteristic curves (AUC) for the prediction of hypertension risk in women and men were 0.721 and 0.652, respectively, according to the logistic regression with wrapper-based variable selection; for hypotension, the corresponding values were 0.675 in women and 0.737 in men, according to the naïve Bayes with wrapper-based variable selection.
The best indicators of the risk of hypertension and the risk of hypotension may differ. The use of combined indices seems to slightly improve the predictive power for both hypertension and hypotension.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Anthropometry</subject><subject>Area Under Curve</subject><subject>Bayesian analysis</subject><subject>Blood pressure</subject><subject>Body fat</subject><subject>Body Mass Index</subject><subject>Body measurements</subject><subject>Body Size</subject><subject>Body Weight</subject><subject>Cardiology</subject><subject>Cardiovascular disease</subject><subject>Circumferences</subject><subject>Comparative analysis</subject><subject>Data mining</subject><subject>Data processing</subject><subject>Dementia</subject><subject>Engineering</subject><subject>Female</subject><subject>Health risk assessment</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Hypertension - pathology</subject><subject>Hypotension</subject><subject>Hypotension - pathology</subject><subject>Indicators</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Male</subject><subject>Mathematics</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Men</subject><subject>Mens health</subject><subject>Metabolic syndrome</subject><subject>Middle Aged</subject><subject>Neck</subject><subject>Obesity</subject><subject>Predictions</subject><subject>Regression analysis</subject><subject>Risk</subject><subject>Risk factors</subject><subject>ROC Curve</subject><subject>Social and Behavioral Sciences</subject><subject>Statistical analysis</subject><subject>Statistical analysis of data</subject><subject>Studies</subject><subject>Type 2 diabetes</subject><subject>Womens health</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2L1DAUhoso7rr6D0QLguDFjPlqm94Iw-LHwMKCX7chTU6nGdumm6Sr--9Nnc4yBQXJRU7Oec6b5PAmyXOM1pgW-O3ejq6X7XqwPawR4oyXxYPkHJeUrHKC6MOT-Cx54v0eoYzyPH-cnBHG8oyx8jzRm1TZbpDOeNuntk5DA-ngQBsVzG0M7U9wU172oXF2sB0EZ1Rq-kiAT2vr0uZuABeg9yZKyF5PCXs8R-EfT5NHtWw9PJv3i-Tbh_dfLz-trq4_bi83VytVEBxWCkBrzJjOuEKYsrrKNCqziimKyhwVuopfwbhGqq6x0izL8pxWBAjmLCMlphfJy4Pu0Fov5gF5gVmsEZpzFontgdBW7sXgTCfdnbDSiD8J63ZCumBUC4JgQrSSXKtCMY5BVgXlKtPAAXMladR6N982Vh1oBX1wsl2ILiu9acTO3gpaIsRQHgVezQLO3ozgwz-ePFM7GV9l-tpGMdUZr8SGFZxnGKPp6-u_UHFp6IyKFqlNzC8a3iwaIhPgV9jJ0Xux_fL5_9nr70v29QnbgGxD4207hmgGvwTZAVTOeu-gvp8cRmJy-HEaYnK4mB0e216cTv2-6Whp-hv5Mvee</recordid><startdate>20140123</startdate><enddate>20140123</enddate><creator>Lee, Bum Ju</creator><creator>Kim, Jong Yeol</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20140123</creationdate><title>A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk</title><author>Lee, Bum Ju ; Kim, Jong Yeol</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c721t-ceedd144d58c0134fb5d095b4c309607db20311f0cff1cd455663b2e218452913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Anthropometry</topic><topic>Area Under Curve</topic><topic>Bayesian analysis</topic><topic>Blood pressure</topic><topic>Body fat</topic><topic>Body Mass Index</topic><topic>Body measurements</topic><topic>Body Size</topic><topic>Body Weight</topic><topic>Cardiology</topic><topic>Cardiovascular disease</topic><topic>Circumferences</topic><topic>Comparative analysis</topic><topic>Data mining</topic><topic>Data processing</topic><topic>Dementia</topic><topic>Engineering</topic><topic>Female</topic><topic>Health risk assessment</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Hypertension - pathology</topic><topic>Hypotension</topic><topic>Hypotension - pathology</topic><topic>Indicators</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Male</topic><topic>Mathematics</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Men</topic><topic>Mens health</topic><topic>Metabolic syndrome</topic><topic>Middle Aged</topic><topic>Neck</topic><topic>Obesity</topic><topic>Predictions</topic><topic>Regression analysis</topic><topic>Risk</topic><topic>Risk factors</topic><topic>ROC Curve</topic><topic>Social and Behavioral Sciences</topic><topic>Statistical analysis</topic><topic>Statistical analysis of data</topic><topic>Studies</topic><topic>Type 2 diabetes</topic><topic>Womens health</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Bum Ju</creatorcontrib><creatorcontrib>Kim, Jong Yeol</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</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>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Bum Ju</au><au>Kim, Jong Yeol</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-01-23</date><risdate>2014</risdate><volume>9</volume><issue>1</issue><spage>e84897</spage><pages>e84897-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.
For 12789 subjects 21-85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.
The best indicator for predicting hypertension was rib circumference in both women (p = <0.0001; OR = 1.813; AUC = 0.669) and men (p = <0.0001; OR = 1.601; AUC = 0.627); for hypotension, the strongest predictor was chest circumference in women (p = <0.0001; OR = 0.541; AUC = 0.657) and neck circumference in men (p = <0.0001; OR = 0.522; AUC = 0.672). In experiments using combined indices, the areas under the receiver operating characteristic curves (AUC) for the prediction of hypertension risk in women and men were 0.721 and 0.652, respectively, according to the logistic regression with wrapper-based variable selection; for hypotension, the corresponding values were 0.675 in women and 0.737 in men, according to the naïve Bayes with wrapper-based variable selection.
The best indicators of the risk of hypertension and the risk of hypotension may differ. The use of combined indices seems to slightly improve the predictive power for both hypertension and hypotension.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24465449</pmid><doi>10.1371/journal.pone.0084897</doi><tpages>e84897</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2014-01, Vol.9 (1), p.e84897 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1491323684 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Adult Aged Aged, 80 and over Algorithms Anthropometry Area Under Curve Bayesian analysis Blood pressure Body fat Body Mass Index Body measurements Body Size Body Weight Cardiology Cardiovascular disease Circumferences Comparative analysis Data mining Data processing Dementia Engineering Female Health risk assessment Humans Hypertension Hypertension - pathology Hypotension Hypotension - pathology Indicators Learning algorithms Machine learning Male Mathematics Medical research Medicine Men Mens health Metabolic syndrome Middle Aged Neck Obesity Predictions Regression analysis Risk Risk factors ROC Curve Social and Behavioral Sciences Statistical analysis Statistical analysis of data Studies Type 2 diabetes Womens health Young Adult |
| title | A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T18%3A30%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20comparison%20of%20the%20predictive%20power%20of%20anthropometric%20indices%20for%20hypertension%20and%20hypotension%20risk&rft.jtitle=PloS%20one&rft.au=Lee,%20Bum%20Ju&rft.date=2014-01-23&rft.volume=9&rft.issue=1&rft.spage=e84897&rft.pages=e84897-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0084897&rft_dat=%3Cgale_plos_%3EA478851101%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1491323684&rft_id=info:pmid/24465449&rft_galeid=A478851101&rft_doaj_id=oai_doaj_org_article_2122dca8dc7c481eab738c5de8e18ca3&rfr_iscdi=true |