Combining LSTM-enhanced features with machine learning algorithms for improved heart failure prediction

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dc.authorid0000-0002-4488-478X
dc.authorid0000-0002-6434-2363
dc.contributor.authorAcar, Züleyha Yılmaz
dc.contributor.authorTok, Ümit
dc.date.accessioned2025-02-13T06:08:18Z
dc.date.available2025-02-13T06:08:18Z
dc.date.issued2024
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIt is well-known that the majority of deaths in the world are caused by heart disease. Therefore, early diagnosis of heart disease is of vital importance. Artificial intelligence techniques that aim to support specialists are among the most effective methods used in the field of health. In this study, in order to improve the detection of heart failure, we proposed a classification scheme to improve heart failure detection by generating new representations of the dataset using the LSTM model (LSTM-enhanced features) and machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), naive bayes (NB)). The LSTM was used to extract deep features that reveal the dependencies among the dataset. The 11 features from 918 data samples in the dataset were re-represented with LSTM and used as 100 LSTM-enhanced features. Experimental results showed that our proposed scheme achieved an accuracy of 92.90%, precision of 94.90%, recall of 92.08%, and F1-score of 93.47%. Performance comparisons with other studies demonstrated that the LSTM-based scheme proposed in this study is applicable to similar datasets.
dc.identifier.citationAcar, Z. Y., Tok, Ü. (2024). Combining LSTM-enhanced features with machine learning algorithms for improved heart failure prediction. Selcuk University Journal of Engineering Sciences, 23 (2), 48-53.
dc.identifier.endpage53
dc.identifier.issn2757-8828
dc.identifier.issue2
dc.identifier.startpage48
dc.identifier.urihttps://hdl.handle.net/20.500.12395/54395
dc.identifier.volume23
dc.institutionauthorAcar, Züleyha Yılmaz
dc.institutionauthorTok, Ümit
dc.institutionauthorid0000-0002-4488-478X
dc.institutionauthorid0000-0002-6434-2363
dc.language.isoen
dc.publisherSelçuk Üniversitesi
dc.relation.ispartofSelcuk University Journal of Engineering Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Features
dc.subjectHeart Failure
dc.subjectLong Short-Term Memory
dc.subjectMachine Learning Algorithms
dc.subjectDerin Özellikler
dc.subjectKalp Yetmezliği
dc.subjectUzun Kısa Süreli Bellek
dc.subjectMakine Öğrenme Algoritmaları
dc.titleCombining LSTM-enhanced features with machine learning algorithms for improved heart failure prediction
dc.typeArticle

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