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Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis

dc.contributor.authorSoto Vergel, Angelo Joseph
dc.contributor.authorMedina Delgado, Byron
dc.contributor.authorpalacios alvarado, wlamyr
dc.date.accessioned2022-11-18T15:51:55Z
dc.date.available2022-11-18T15:51:55Z
dc.date.issued2021-06-09
dc.identifier.urihttps://repositorio.ufps.edu.co/handle/ufps/6539
dc.description.abstractThis article evaluates autoregressive modeling as a feature extraction method in a database of chromatographic signals from urine samples for non-invasive diagnostic support of prostate cancer in response to the research question: Can chromatographic signals from urine be characterized and used as a non-invasive method for cancer diagnosis? For this purpose, a database of 18 patients, 9 diagnosed with prostate cancer and 9 control patients, is consolidated, statistical methods are implemented to generate autoregressive coefficients from the data signals, and finally, the principal component analysis technique is applied for crossclass classification. As a result, a correct classification was obtained in the total number of samples validating the autoregressive modelling as a feature extraction method in contrast to the conventional methodology usually followed in chromatographic signal processing.eng
dc.format.extent07 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherJournal of Physics: Conference Seriesspa
dc.relation.ispartofJournal of Physics: Conference Series. Vol.1938 N°.1. (2021)
dc.rightsContent from this work may be used under the terms of theCreative Commons Attribution 3.0 licenceeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://iopscience.iop.org/article/10.1088/1742-6596/1938/1/012011/pdfspa
dc.titleAutoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosiseng
dc.typeArtículo de revistaspa
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dc.contributor.corporatenameJournal of Physics: Conference Seriesspa
dc.identifier.doi10.1088/1742-6596/1938/1/012011
dc.publisher.placeReino Unidospa
dc.relation.citationeditionVol. 1938 N°.1. (2021)spa
dc.relation.citationendpage6spa
dc.relation.citationissue1(2021)spa
dc.relation.citationstartpage1spa
dc.relation.citationvolume1938spa
dc.relation.citesSoto-Vergel, A. J., Medina-Delgado, B., & Palacios-Alvarado, W. L. A. M. Y. R. (2021, May). Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis. In Journal of Physics: Conference Series (Vol. 1938, No. 1, p. 012011). IOP Publishing.
dc.relation.ispartofjournalJournal of Physics: Conference Seriesspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.subject.proposalChromatographyeng
dc.subject.proposalClassification (of information)eng
dc.subject.proposalDiagnosiseng
dc.subject.proposalDiseaseseng
dc.subject.proposalFeature extractioneng
dc.subject.proposalNoninvasive medical procedureseng
dc.subject.proposalSignal processingeng
dc.subject.proposalUrologyeng
dc.subject.proposalAuto regressive modelseng
dc.subject.proposalAutoregressive coefficienteng
dc.subject.proposalAutoregressive modellingeng
dc.subject.proposalChromatographic signalseng
dc.subject.proposalFeature extraction methodseng
dc.subject.proposalNon-invasive diagnosticseng
dc.subject.proposalNoninvasive methodseng
dc.subject.proposalResearch questionseng
dc.subject.proposalExtractioneng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
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dc.type.driverinfo:eu-repo/semantics/articlespa
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oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa


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