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Inteligent software system for metabolic syndrome diagnostics

dc.contributor.advisorKupusinac, Aleksandar
dc.contributor.advisorDoroslovački, Rade
dc.contributor.otherIvetić, Dragan
dc.contributor.otherMalbaški, Dušan
dc.contributor.otherStokić, Edita
dc.contributor.otherĆulibrk, Dubravko
dc.contributor.otherDoroslovački, Rade
dc.contributor.otherKupusinac, Aleksandar
dc.creatorIvanović, Darko
dc.date.accessioned2018-04-23T08:33:35Z
dc.date.available2018-04-23T08:33:35Z
dc.date.available2020-07-03T14:08:33Z
dc.date.issued2018-04-16
dc.identifier.urihttps://nardus.mpn.gov.rs/handle/123456789/9340
dc.identifier.urihttps://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija151670035989419.pdf?controlNumber=(BISIS)107048&fileName=151670035989419.pdf&id=10889&source=NaRDuS&language=srsr
dc.identifier.urihttps://www.cris.uns.ac.rs/record.jsf?recordId=107048&source=NaRDuS&language=srsr
dc.identifier.urihttps://www.cris.uns.ac.rs/DownloadFileServlet/IzvestajKomisije151670038559881.pdf?controlNumber=(BISIS)107048&fileName=151670038559881.pdf&id=10890&source=NaRDuS&language=srsr
dc.description.abstractDoktorska disertacija razmatra problem algoritamske dijagnostike metaboličkog sindroma na osnovu lako merljivih parametara: pol, starosna dob, indeks telesne mase, odnos obima struka i visine, sistolni i dijastolni krvni pritisak. U istraživanju su primenjene i eksperimentalno ispitane tri različite metode mašinskog učenja: stabla odluke, linearna regresija i veštačke neuronske mreže. Pokazano je da veštačke neuronske mreže daju visok nivo prediktivnih vrednosti dovoljan za primenu u praksi. Korišćenjem dobijenog rezultata definisan je i implementiran inteligentni softverski sistem za dijagnostiku metaboličkog sindroma.sr
dc.description.abstractThe doctoral dissertation examines the problem of algorithmic diagnostics of the metabolic syndrome based on easily measurable parameters: sex, age, body mass index, waist and height ratio, systolic and diastolic blood pressure. In the study, three different methods of machine learning were applied and experimentally examined: decision trees, linear regression and artificial neural networks. It has been shown that artificial neural networks give a high level of predictive value sufficient to be applied in practice. Using the obtained result, an intelligent software system for the diagnosis of metabolic syndrome has been defined and implemented.en
dc.languagesr (latin script)
dc.publisherУниверзитет у Новом Саду, Факултет техничких наукаsr
dc.rightsopenAccessen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceУниверзитет у Новом Садуsr
dc.subjectMašinsko učenjesr
dc.subjectMachine Learningen
dc.subjectMetabolički sindromsr
dc.subjectDijagnostikasr
dc.subjectVeštačkeneuronske mrežesr
dc.subjectStabla odlučivanjasr
dc.subjectLinearna regresijasr
dc.subjectMetabolic Syndromeen
dc.subjectDiagnosticsen
dc.subjectArtificial NeuralNetworksen
dc.subjectDecision Treesen
dc.subjectLinear Regressionen
dc.titleInteligentni softverski sistem za dijagnostiku metaboličkog sindromasr
dc.title.alternativeInteligent software system for metabolic syndrome diagnosticsen
dc.typedoctoralThesisen
dc.rights.licenseBY-NC-ND
dcterms.abstractКупусинац, Aлександар; Дорословачки, Раде; Стокић, Едита; Дорословачки, Раде; Купусинац, Aлександар; Ћулибрк, Дубравко; Иветић, Драган; Малбашки, Душан; Ивановић, Дарко; Интелигентни софтверски систем за дијагностику метаболичког синдрома; Интелигентни софтверски систем за дијагностику метаболичког синдрома;
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/42042/IzvestajKomisije.pdf
dc.identifier.fulltexthttps://nardus.mpn.gov.rs/bitstream/id/42041/Disertacija.pdf
dc.identifier.fulltexthttps://nardus.mpn.gov.rs/bitstream/id/42042/IzvestajKomisije.pdf
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/42041/Disertacija.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_nardus_9340


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