Predicting Mortality in Patients with Chronic Heart Failure

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but.event.date29.04.2025cs
but.event.titleSTUDENT EEICT 2025cs
dc.contributor.authorHýl, Jan
dc.contributor.authorPchálková, Aneta
dc.date.accessioned2025-07-30T10:00:54Z
dc.date.available2025-07-30T10:00:54Z
dc.date.issued2025cs
dc.description.abstractChronic heart failure affects many people worldwide. A random forest model was created in this study to help predict if the patients die or survive. The model was based on the data from MUSIC database and on data extracted from highresolution ECG records in the database. In total, 40 features were used for the training. The model achieved 0.50 F1 score, 0.73 AUROC and 0.60 AUPRC on test set. The most significant features were ”Pro BNP (ng/L)” and ”Urea (mg/dL)”.en
dc.formattextcs
dc.format.extent111-114cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 31st Conference STUDENT EEICT 2025: General papers. s. 111-114. ISBN 978-80-214-6321-9cs
dc.identifier.isbn978-80-214-6321-9
dc.identifier.urihttps://hdl.handle.net/11012/255255
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 31st Conference STUDENT EEICT 2025: General papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectChronic Heart Failureen
dc.subjectrandom foresten
dc.subjectpredictionen
dc.subjectdata processingen
dc.subjectunivariate analysisen
dc.subjectperformanceen
dc.subjectfeature importanceen
dc.titlePredicting Mortality in Patients with Chronic Heart Failureen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs

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