Karakuş, Mücella ÖzbayEr, Orhan2023-03-222023-03-2220220941-06431433-3058https://doi.org/10.1007/s00521-022-07201-9https://hdl.handle.net/20.500.14034/614Cardiovascular 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.eninfo:eu-repo/semantics/closedAccessMachine learningHeart failureSurvival predictionSupport vector machineArtificial neural networksIn-Hospital MortalitySerum CreatinineClassificationDiagnosisPrognosisSelectionArticle10.1007/s00521-022-07201-934161389513908Q2WOS:0007830371000012-s2.0-85128205292Q1