A comparative study on prediction of survival event of heart failure patients using machine learning algorithms

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dc.contributor.authorKarakuş, Mücella Özbay
dc.contributor.authorEr, Orhan
dc.date.accessioned2023-03-22T19:47:19Z
dc.date.available2023-03-22T19:47:19Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractCardiovascular diseases cause approximately 17 million deaths each year and 31% of deaths worldwide. These diseases generally occur as myocardial infarction and heart failure. The survival status, which we used as a target in our classification study, indicates that the patient died or survived before the end of the follow-up period, which is a mean of 130 days. Various machine learning classifiers have been preferred to both predict survival of patients and rank the characteristics corresponding to the most important risk factors. For this purpose, the data set that is occurred totally 299 samples is traditionally divided into 70% for training and 30% for test cluster to be used in machine learning algorithms, with have been analyzed with many methods such as Artificial Neural Networks, Fine Gaussian SVM, Fine KNN, Weighted KNN, Subspace KNN, Boosted Trees, and Bagged Trees. As a result, according to the data obtained, it has been seen that there are algorithms that can predict heart failure diagnosis with full accuracy (100%). Thus, it was concluded that it is appropriate to use machine learning algorithms to predict whether a heart failure patient will survive. This study has the potential to be used as a new supportive tool for doctors when predicting whether a heart failure patient will survive.en_US
dc.identifier.doi10.1007/s00521-022-07201-9
dc.identifier.endpage13908en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-85128205292en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage13895en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07201-9
dc.identifier.urihttps://hdl.handle.net/20.500.14034/614
dc.identifier.volume34en_US
dc.identifier.wosWOS:000783037100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.journalNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectHeart failureen_US
dc.subjectSurvival predictionen_US
dc.subjectSupport vector machineen_US
dc.subjectArtificial neural networksen_US
dc.subjectIn-Hospital Mortalityen_US
dc.subjectSerum Creatinineen_US
dc.subjectClassificationen_US
dc.subjectDiagnosisen_US
dc.subjectPrognosisen_US
dc.subjectSelectionen_US
dc.titleA comparative study on prediction of survival event of heart failure patients using machine learning algorithmsen_US
dc.typeArticleen_US

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