Mapping risk of ischemic heart disease using machine learning in a Brazilian state
Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD ba...
Gespeichert in:
| Veröffentlicht in: | PloS one 2020-12, Vol.15 (12), p.e0243558 |
|---|---|
| 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 | 12 |
| container_start_page | e0243558 |
| container_title | PloS one |
| container_volume | 15 |
| creator | Bergamini, Marcela Iora, Pedro Henrique Rocha, Thiago Augusto Hernandes Tchuisseu, Yolande Pokam Dutra, Amanda de Carvalho Scheidt, João Felipe Herman Costa Nihei, Oscar Kenji de Barros Carvalho, Maria Dalva Staton, Catherine Ann Vissoci, João Ricardo Nickenig de Andrade, Luciano |
| description | Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran's I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities' guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná. |
| doi_str_mv | 10.1371/journal.pone.0243558 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2469061736</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A644514338</galeid><doaj_id>oai_doaj_org_article_2548b132c992414cbe25de19277d566a</doaj_id><sourcerecordid>A644514338</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-643499f664860cb7f7727f70ddcc3f0d83f2fc9f779558e0d80cbedbb3443ff73</originalsourceid><addsrcrecordid>eNqNkttu1DAQhiMEoqXwBggiISG42MWnOPENUqk4rFRUqRxuLa9j73rx2oudVIWnZ8Km1Qb1AkVKot_fzHhm_qJ4itEc0xq_2cQ-BeXnuxjMHBFGq6q5VxxjQcmME0TvH_wfFY9y3iBU0Ybzh8URpRRhVuHj4vKz2u1cWJXJ5R9ltKXLem22Tpdro1JXti4blU3Z5wHaKr12wZQezsIguFCq8l1Sv513KpS5U515XDywymfzZPyeFN8-vP969ml2fvFxcXZ6PtNckG7GGWVCWM5Zw5Fe1rauCbxQ22pNLWobaonVAmQBnRkQgDLtckkZo9bW9KR4vs-78zHLcRxZEsYF4rimHIjFnmij2shdcluVfsmonPwrxLSS0KPT3khSsWaJKdFCEIYZVCJVa7Agdd1WnCvI9Xas1i-3ptUmdEn5SdLpSXBruYpXEtpqSD1c5tWYIMWfvcmd3MKsjfcqmNgP964x46gSBNAX_6B3dzdSKwUNuGAj1NVDUnnKGWyXUdoANb-DgqcdtgzesQ70ScDrSQAwnbnuVqrPWS6-XP4_e_F9yr48YMFdvlvn6PvOxZCnINuDOsWck7G3Q8ZIDta_mYYcrC9H60PYs8MF3QbdeJ3-ATMB_P8</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2469061736</pqid></control><display><type>article</type><title>Mapping risk of ischemic heart disease using machine learning in a Brazilian state</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS)</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Bergamini, Marcela ; Iora, Pedro Henrique ; Rocha, Thiago Augusto Hernandes ; Tchuisseu, Yolande Pokam ; Dutra, Amanda de Carvalho ; Scheidt, João Felipe Herman Costa ; Nihei, Oscar Kenji ; de Barros Carvalho, Maria Dalva ; Staton, Catherine Ann ; Vissoci, João Ricardo Nickenig ; de Andrade, Luciano</creator><contributor>Tolkacheva, Elena G.</contributor><creatorcontrib>Bergamini, Marcela ; Iora, Pedro Henrique ; Rocha, Thiago Augusto Hernandes ; Tchuisseu, Yolande Pokam ; Dutra, Amanda de Carvalho ; Scheidt, João Felipe Herman Costa ; Nihei, Oscar Kenji ; de Barros Carvalho, Maria Dalva ; Staton, Catherine Ann ; Vissoci, João Ricardo Nickenig ; de Andrade, Luciano ; Tolkacheva, Elena G.</creatorcontrib><description>Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran's I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities' guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0243558</identifier><identifier>PMID: 33301451</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Biology and Life Sciences ; Brazil - epidemiology ; Cardiovascular disease ; Cardiovascular diseases ; Clusters ; Computer and Information Sciences ; Coronary artery disease ; Data analysis ; Distribution ; Emergency medical care ; Emergency medical services ; Geographic information systems ; Health care ; Health risks ; Health sciences ; Heart ; Heart diseases ; Human Development Index ; Humans ; Impact prediction ; Information systems ; Ischemia ; Learning algorithms ; Machine Learning ; Mathematical functions ; Medicine ; Medicine and Health Sciences ; Models, Theoretical ; Mortality ; Myocardial ischemia ; Myocardial Ischemia - epidemiology ; Myocardial Ischemia - mortality ; Myocardial Ischemia - prevention & control ; Patient outcomes ; People and places ; Physical Sciences ; Population ; Public health ; Research and Analysis Methods ; Risk analysis ; Risk Assessment - methods ; Risk Factors ; Root-mean-square errors ; Support vector machines</subject><ispartof>PloS one, 2020-12, Vol.15 (12), p.e0243558</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Bergamini et al. 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>2020 Bergamini et al 2020 Bergamini et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-643499f664860cb7f7727f70ddcc3f0d83f2fc9f779558e0d80cbedbb3443ff73</citedby><cites>FETCH-LOGICAL-c692t-643499f664860cb7f7727f70ddcc3f0d83f2fc9f779558e0d80cbedbb3443ff73</cites><orcidid>0000-0002-8517-0660 ; 0000-0003-2638-5407</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/PMC7728276/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728276/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23865,27923,27924,53790,53792,79371,79372</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33301451$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Tolkacheva, Elena G.</contributor><creatorcontrib>Bergamini, Marcela</creatorcontrib><creatorcontrib>Iora, Pedro Henrique</creatorcontrib><creatorcontrib>Rocha, Thiago Augusto Hernandes</creatorcontrib><creatorcontrib>Tchuisseu, Yolande Pokam</creatorcontrib><creatorcontrib>Dutra, Amanda de Carvalho</creatorcontrib><creatorcontrib>Scheidt, João Felipe Herman Costa</creatorcontrib><creatorcontrib>Nihei, Oscar Kenji</creatorcontrib><creatorcontrib>de Barros Carvalho, Maria Dalva</creatorcontrib><creatorcontrib>Staton, Catherine Ann</creatorcontrib><creatorcontrib>Vissoci, João Ricardo Nickenig</creatorcontrib><creatorcontrib>de Andrade, Luciano</creatorcontrib><title>Mapping risk of ischemic heart disease using machine learning in a Brazilian state</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran's I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities' guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná.</description><subject>Algorithms</subject><subject>Biology and Life Sciences</subject><subject>Brazil - epidemiology</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Clusters</subject><subject>Computer and Information Sciences</subject><subject>Coronary artery disease</subject><subject>Data analysis</subject><subject>Distribution</subject><subject>Emergency medical care</subject><subject>Emergency medical services</subject><subject>Geographic information systems</subject><subject>Health care</subject><subject>Health risks</subject><subject>Health sciences</subject><subject>Heart</subject><subject>Heart diseases</subject><subject>Human Development Index</subject><subject>Humans</subject><subject>Impact prediction</subject><subject>Information systems</subject><subject>Ischemia</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Mathematical functions</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Models, Theoretical</subject><subject>Mortality</subject><subject>Myocardial ischemia</subject><subject>Myocardial Ischemia - epidemiology</subject><subject>Myocardial Ischemia - mortality</subject><subject>Myocardial Ischemia - prevention & control</subject><subject>Patient outcomes</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>Public health</subject><subject>Research and Analysis Methods</subject><subject>Risk analysis</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>Root-mean-square errors</subject><subject>Support vector machines</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</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><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkttu1DAQhiMEoqXwBggiISG42MWnOPENUqk4rFRUqRxuLa9j73rx2oudVIWnZ8Km1Qb1AkVKot_fzHhm_qJ4itEc0xq_2cQ-BeXnuxjMHBFGq6q5VxxjQcmME0TvH_wfFY9y3iBU0Ybzh8URpRRhVuHj4vKz2u1cWJXJ5R9ltKXLem22Tpdro1JXti4blU3Z5wHaKr12wZQezsIguFCq8l1Sv513KpS5U515XDywymfzZPyeFN8-vP969ml2fvFxcXZ6PtNckG7GGWVCWM5Zw5Fe1rauCbxQ22pNLWobaonVAmQBnRkQgDLtckkZo9bW9KR4vs-78zHLcRxZEsYF4rimHIjFnmij2shdcluVfsmonPwrxLSS0KPT3khSsWaJKdFCEIYZVCJVa7Agdd1WnCvI9Xas1i-3ptUmdEn5SdLpSXBruYpXEtpqSD1c5tWYIMWfvcmd3MKsjfcqmNgP964x46gSBNAX_6B3dzdSKwUNuGAj1NVDUnnKGWyXUdoANb-DgqcdtgzesQ70ScDrSQAwnbnuVqrPWS6-XP4_e_F9yr48YMFdvlvn6PvOxZCnINuDOsWck7G3Q8ZIDta_mYYcrC9H60PYs8MF3QbdeJ3-ATMB_P8</recordid><startdate>20201210</startdate><enddate>20201210</enddate><creator>Bergamini, Marcela</creator><creator>Iora, Pedro Henrique</creator><creator>Rocha, Thiago Augusto Hernandes</creator><creator>Tchuisseu, Yolande Pokam</creator><creator>Dutra, Amanda de Carvalho</creator><creator>Scheidt, João Felipe Herman Costa</creator><creator>Nihei, Oscar Kenji</creator><creator>de Barros Carvalho, Maria Dalva</creator><creator>Staton, Catherine Ann</creator><creator>Vissoci, João Ricardo Nickenig</creator><creator>de Andrade, Luciano</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>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8517-0660</orcidid><orcidid>https://orcid.org/0000-0003-2638-5407</orcidid></search><sort><creationdate>20201210</creationdate><title>Mapping risk of ischemic heart disease using machine learning in a Brazilian state</title><author>Bergamini, Marcela ; Iora, Pedro Henrique ; Rocha, Thiago Augusto Hernandes ; Tchuisseu, Yolande Pokam ; Dutra, Amanda de Carvalho ; Scheidt, João Felipe Herman Costa ; Nihei, Oscar Kenji ; de Barros Carvalho, Maria Dalva ; Staton, Catherine Ann ; Vissoci, João Ricardo Nickenig ; de Andrade, Luciano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-643499f664860cb7f7727f70ddcc3f0d83f2fc9f779558e0d80cbedbb3443ff73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Biology and Life Sciences</topic><topic>Brazil - epidemiology</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Clusters</topic><topic>Computer and Information Sciences</topic><topic>Coronary artery disease</topic><topic>Data analysis</topic><topic>Distribution</topic><topic>Emergency medical care</topic><topic>Emergency medical services</topic><topic>Geographic information systems</topic><topic>Health care</topic><topic>Health risks</topic><topic>Health sciences</topic><topic>Heart</topic><topic>Heart diseases</topic><topic>Human Development Index</topic><topic>Humans</topic><topic>Impact prediction</topic><topic>Information systems</topic><topic>Ischemia</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Mathematical functions</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Models, Theoretical</topic><topic>Mortality</topic><topic>Myocardial ischemia</topic><topic>Myocardial Ischemia - epidemiology</topic><topic>Myocardial Ischemia - mortality</topic><topic>Myocardial Ischemia - prevention & control</topic><topic>Patient outcomes</topic><topic>People and places</topic><topic>Physical Sciences</topic><topic>Population</topic><topic>Public health</topic><topic>Research and Analysis Methods</topic><topic>Risk analysis</topic><topic>Risk Assessment - methods</topic><topic>Risk Factors</topic><topic>Root-mean-square errors</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bergamini, Marcela</creatorcontrib><creatorcontrib>Iora, Pedro Henrique</creatorcontrib><creatorcontrib>Rocha, Thiago Augusto Hernandes</creatorcontrib><creatorcontrib>Tchuisseu, Yolande Pokam</creatorcontrib><creatorcontrib>Dutra, Amanda de Carvalho</creatorcontrib><creatorcontrib>Scheidt, João Felipe Herman Costa</creatorcontrib><creatorcontrib>Nihei, Oscar Kenji</creatorcontrib><creatorcontrib>de Barros Carvalho, Maria Dalva</creatorcontrib><creatorcontrib>Staton, Catherine Ann</creatorcontrib><creatorcontrib>Vissoci, João Ricardo Nickenig</creatorcontrib><creatorcontrib>de Andrade, Luciano</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>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</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>Bergamini, Marcela</au><au>Iora, Pedro Henrique</au><au>Rocha, Thiago Augusto Hernandes</au><au>Tchuisseu, Yolande Pokam</au><au>Dutra, Amanda de Carvalho</au><au>Scheidt, João Felipe Herman Costa</au><au>Nihei, Oscar Kenji</au><au>de Barros Carvalho, Maria Dalva</au><au>Staton, Catherine Ann</au><au>Vissoci, João Ricardo Nickenig</au><au>de Andrade, Luciano</au><au>Tolkacheva, Elena G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping risk of ischemic heart disease using machine learning in a Brazilian state</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-12-10</date><risdate>2020</risdate><volume>15</volume><issue>12</issue><spage>e0243558</spage><pages>e0243558-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran's I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities' guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33301451</pmid><doi>10.1371/journal.pone.0243558</doi><tpages>e0243558</tpages><orcidid>https://orcid.org/0000-0002-8517-0660</orcidid><orcidid>https://orcid.org/0000-0003-2638-5407</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2020-12, Vol.15 (12), p.e0243558 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2469061736 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Biology and Life Sciences Brazil - epidemiology Cardiovascular disease Cardiovascular diseases Clusters Computer and Information Sciences Coronary artery disease Data analysis Distribution Emergency medical care Emergency medical services Geographic information systems Health care Health risks Health sciences Heart Heart diseases Human Development Index Humans Impact prediction Information systems Ischemia Learning algorithms Machine Learning Mathematical functions Medicine Medicine and Health Sciences Models, Theoretical Mortality Myocardial ischemia Myocardial Ischemia - epidemiology Myocardial Ischemia - mortality Myocardial Ischemia - prevention & control Patient outcomes People and places Physical Sciences Population Public health Research and Analysis Methods Risk analysis Risk Assessment - methods Risk Factors Root-mean-square errors Support vector machines |
| title | Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T16%3A22%3A14IST&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=Mapping%20risk%20of%20ischemic%20heart%20disease%20using%20machine%20learning%20in%20a%20Brazilian%20state&rft.jtitle=PloS%20one&rft.au=Bergamini,%20Marcela&rft.date=2020-12-10&rft.volume=15&rft.issue=12&rft.spage=e0243558&rft.pages=e0243558-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0243558&rft_dat=%3Cgale_plos_%3EA644514338%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=2469061736&rft_id=info:pmid/33301451&rft_galeid=A644514338&rft_doaj_id=oai_doaj_org_article_2548b132c992414cbe25de19277d566a&rfr_iscdi=true |