Classification of Patients with Heart Failure

dc.contributor.authorBayrak, Tuncay
dc.contributor.authorOgul, Hasan
dc.contributor.orcIDhttps://orcid.org/0000-0001-6826-4350en_US
dc.contributor.researcherIDU-4603-2019en_US
dc.date.accessioned2024-03-19T13:08:17Z
dc.date.available2024-03-19T13:08:17Z
dc.date.issued2014
dc.description.abstractEchocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.en_US
dc.identifier.urihttp://hdl.handle.net/11727/11889
dc.identifier.wos000381577500021en_US
dc.language.isoturen_US
dc.relation.journal2014 18TH NATIONAL BIOMEDICAL ENGINEERING MEETING (BIYOMUT)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleClassification of Patients with Heart Failureen_US
dc.typeconferenceObjecten_US

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