Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening
In order to improve the costs/benefits ratio of breast cancer (BC) screenings, the author evaluated the performance of a back-propagation artificial neural network (ANN) to predict an outcome (cancer/not cancer) to be used as classificator. Networks were trained on data from familial history of canc...
Gespeichert in:
| Veröffentlicht in: | Artificial intelligence in medicine 1999-07, Vol.16 (3), p.299-309 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 309 |
|---|---|
| container_issue | 3 |
| container_start_page | 299 |
| container_title | Artificial intelligence in medicine |
| container_volume | 16 |
| creator | Ronco, Alvaro L. |
| description | In order to improve the costs/benefits ratio of breast cancer (BC) screenings, the author evaluated the performance of a back-propagation artificial neural network (ANN) to predict an outcome (cancer/not cancer) to be used as classificator. Networks were trained on data from familial history of cancer, and sociodemographic, gynecoobstetric and dietary variables. The ANN achieved up to 94.04% of positive predictive value and 97.60% of negative predictive value. Results could operate as guidelines for preselecting women who would be considered as a BC high-risk subpopulation. The procedure—not only based on age factor, but on a multifactorial basis—appears to be a promising method of achieving a more efficient detection of preclinical, asymptomatic BC cases. |
| doi_str_mv | 10.1016/S0933-3657(99)00004-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_69868474</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0933365799000044</els_id><sourcerecordid>401576</sourcerecordid><originalsourceid>FETCH-LOGICAL-c423t-ca592d306434d1245c73f083586ebd955b0faa35ff604a73a23e208a73572c943</originalsourceid><addsrcrecordid>eNqFkc1ulCEUhonR2Gn1EjSsjF18Ch__bhrT-Jc0caFdEwYODfoNVGDaeA9etMxMY9wNm0PCc855eV-EXlDyhhIq334jhrGJSaFeG3NOxuETf4RWVCs2zVqSx2j1DzlBp639GIziVD5FJ5QwoxiRK_TnugEuEbvaU0w-uQVn2NZ96fel_mw4ZbwpAZaUb7BrrQyop5Lbri2k5mvapOxyx9H5Xmp7h3u5dzU07PJo7rAs6QbGe4MFfE93gNcVXOvYu-yh4jECII_xz9CT6JYGzx_qGbr--OH75efp6uunL5fvrybPZ9Yn74SZw5DPGQ905sIrFolmQktYByPEmkTnmIhREu4UczODmehxE2r2hrMz9Oow97aWX1to3W7GP4ZOl6Fsm5VGS83VcXBWdKZKiOMgZVoyrQcoDqCvpbUK0d4O_1z9bSmxu2DtPli7S80aY_fB2p2Slw8LtusNhP-6DkkO4OIAwDDuLkG1zScY_oZUh-s2lHRkxV8hLLTx</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>21386388</pqid></control><display><type>article</type><title>Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals Complete</source><creator>Ronco, Alvaro L.</creator><creatorcontrib>Ronco, Alvaro L.</creatorcontrib><description>In order to improve the costs/benefits ratio of breast cancer (BC) screenings, the author evaluated the performance of a back-propagation artificial neural network (ANN) to predict an outcome (cancer/not cancer) to be used as classificator. Networks were trained on data from familial history of cancer, and sociodemographic, gynecoobstetric and dietary variables. The ANN achieved up to 94.04% of positive predictive value and 97.60% of negative predictive value. Results could operate as guidelines for preselecting women who would be considered as a BC high-risk subpopulation. The procedure—not only based on age factor, but on a multifactorial basis—appears to be a promising method of achieving a more efficient detection of preclinical, asymptomatic BC cases.</description><identifier>ISSN: 0933-3657</identifier><identifier>EISSN: 1873-2860</identifier><identifier>DOI: 10.1016/S0933-3657(99)00004-4</identifier><identifier>PMID: 10397306</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Backpropagation ; Breast cancer ; Breast Neoplasms - diagnosis ; Cancer screening ; Computer simulation ; Epidemiology ; Female ; Humans ; Mass Screening - methods ; Neural networks ; Neural Networks (Computer) ; Risk assessment ; Risk factors</subject><ispartof>Artificial intelligence in medicine, 1999-07, Vol.16 (3), p.299-309</ispartof><rights>1999 Elsevier Science B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c423t-ca592d306434d1245c73f083586ebd955b0faa35ff604a73a23e208a73572c943</citedby><cites>FETCH-LOGICAL-c423t-ca592d306434d1245c73f083586ebd955b0faa35ff604a73a23e208a73572c943</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S0933-3657(99)00004-4$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27922,27923,45993</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/10397306$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ronco, Alvaro L.</creatorcontrib><title>Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening</title><title>Artificial intelligence in medicine</title><addtitle>Artif Intell Med</addtitle><description>In order to improve the costs/benefits ratio of breast cancer (BC) screenings, the author evaluated the performance of a back-propagation artificial neural network (ANN) to predict an outcome (cancer/not cancer) to be used as classificator. Networks were trained on data from familial history of cancer, and sociodemographic, gynecoobstetric and dietary variables. The ANN achieved up to 94.04% of positive predictive value and 97.60% of negative predictive value. Results could operate as guidelines for preselecting women who would be considered as a BC high-risk subpopulation. The procedure—not only based on age factor, but on a multifactorial basis—appears to be a promising method of achieving a more efficient detection of preclinical, asymptomatic BC cases.</description><subject>Backpropagation</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnosis</subject><subject>Cancer screening</subject><subject>Computer simulation</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Humans</subject><subject>Mass Screening - methods</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Risk assessment</subject><subject>Risk factors</subject><issn>0933-3657</issn><issn>1873-2860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc1ulCEUhonR2Gn1EjSsjF18Ch__bhrT-Jc0caFdEwYODfoNVGDaeA9etMxMY9wNm0PCc855eV-EXlDyhhIq334jhrGJSaFeG3NOxuETf4RWVCs2zVqSx2j1DzlBp639GIziVD5FJ5QwoxiRK_TnugEuEbvaU0w-uQVn2NZ96fel_mw4ZbwpAZaUb7BrrQyop5Lbri2k5mvapOxyx9H5Xmp7h3u5dzU07PJo7rAs6QbGe4MFfE93gNcVXOvYu-yh4jECII_xz9CT6JYGzx_qGbr--OH75efp6uunL5fvrybPZ9Yn74SZw5DPGQ905sIrFolmQktYByPEmkTnmIhREu4UczODmehxE2r2hrMz9Oow97aWX1to3W7GP4ZOl6Fsm5VGS83VcXBWdKZKiOMgZVoyrQcoDqCvpbUK0d4O_1z9bSmxu2DtPli7S80aY_fB2p2Slw8LtusNhP-6DkkO4OIAwDDuLkG1zScY_oZUh-s2lHRkxV8hLLTx</recordid><startdate>19990701</startdate><enddate>19990701</enddate><creator>Ronco, Alvaro L.</creator><general>Elsevier B.V</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>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>19990701</creationdate><title>Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening</title><author>Ronco, Alvaro L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c423t-ca592d306434d1245c73f083586ebd955b0faa35ff604a73a23e208a73572c943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Backpropagation</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnosis</topic><topic>Cancer screening</topic><topic>Computer simulation</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Humans</topic><topic>Mass Screening - methods</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Risk assessment</topic><topic>Risk factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ronco, Alvaro L.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Artificial intelligence in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ronco, Alvaro L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening</atitle><jtitle>Artificial intelligence in medicine</jtitle><addtitle>Artif Intell Med</addtitle><date>1999-07-01</date><risdate>1999</risdate><volume>16</volume><issue>3</issue><spage>299</spage><epage>309</epage><pages>299-309</pages><issn>0933-3657</issn><eissn>1873-2860</eissn><abstract>In order to improve the costs/benefits ratio of breast cancer (BC) screenings, the author evaluated the performance of a back-propagation artificial neural network (ANN) to predict an outcome (cancer/not cancer) to be used as classificator. Networks were trained on data from familial history of cancer, and sociodemographic, gynecoobstetric and dietary variables. The ANN achieved up to 94.04% of positive predictive value and 97.60% of negative predictive value. Results could operate as guidelines for preselecting women who would be considered as a BC high-risk subpopulation. The procedure—not only based on age factor, but on a multifactorial basis—appears to be a promising method of achieving a more efficient detection of preclinical, asymptomatic BC cases.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>10397306</pmid><doi>10.1016/S0933-3657(99)00004-4</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0933-3657 |
| ispartof | Artificial intelligence in medicine, 1999-07, Vol.16 (3), p.299-309 |
| issn | 0933-3657 1873-2860 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_69868474 |
| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Backpropagation Breast cancer Breast Neoplasms - diagnosis Cancer screening Computer simulation Epidemiology Female Humans Mass Screening - methods Neural networks Neural Networks (Computer) Risk assessment Risk factors |
| title | Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T06%3A02%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Use%20of%20artificial%20neural%20networks%20in%20modeling%20associations%20of%20discriminant%20factors:%20towards%20an%20intelligent%20selective%20breast%20cancer%20screening&rft.jtitle=Artificial%20intelligence%20in%20medicine&rft.au=Ronco,%20Alvaro%20L.&rft.date=1999-07-01&rft.volume=16&rft.issue=3&rft.spage=299&rft.epage=309&rft.pages=299-309&rft.issn=0933-3657&rft.eissn=1873-2860&rft_id=info:doi/10.1016/S0933-3657(99)00004-4&rft_dat=%3Cproquest_cross%3E401576%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=21386388&rft_id=info:pmid/10397306&rft_els_id=S0933365799000044&rfr_iscdi=true |