<p>Doktorska disertacija razmatra problem algoritamske dijagnostike<br />metaboličkog sindroma na osnovu lako merljivih parametara: pol,<br />starosna dob, indeks telesne mase, odnos obima struka i visine,<br />sistolni i dijastolni krvni pritisak. U istraživanju su primenjene i<br />eksperimentalno ispitane tri različite metode mašinskog učenja:<br />stabla odluke, linearna regresija i veštačke neuronske mreže.<br />Pokazano je da veštačke neuronske mreže daju visok nivo<br />prediktivnih vrednosti dovoljan za primenu u praksi. Korišćenjem<br />dobijenog rezultata definisan je i implementiran inteligentni<br />softverski sistem za dijagnostiku metaboličkog sindroma.</p> / <p>The doctoral dissertation examines the problem of algorithmic diagnostics of<br />the metabolic syndrome based on easily measurable parameters: sex, age,<br />body mass index, waist and height ratio, systolic and diastolic blood<br />pressure. In the study, three different methods of machine learning were<br />applied and experimentally examined: decision trees, linear regression and<br />artificial neural networks. It has been shown that artificial neural networks<br />give a high level of predictive value sufficient to be applied in practice. Using<br />the obtained result, an intelligent software system for the diagnosis of<br />metabolic syndrome has been defined and implemented.</p>
Identifer | oai:union.ndltd.org:uns.ac.rs/oai:CRISUNS:(BISIS)107048 |
Date | 16 April 2018 |
Creators | Ivanović Darko |
Contributors | Kupusinac Aleksandar, Doroslovački Rade, Ivetić Dragan, Malbaški Dušan, Stokić Edita, Ćulibrk Dubravko |
Publisher | Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, University of Novi Sad, Faculty of Technical Sciences at Novi Sad |
Source Sets | University of Novi Sad |
Language | Serbian |
Detected Language | Unknown |
Type | PhD thesis |
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