Combining LSTM-enhanced features with machine learning algorithms for improved heart failure prediction
Yükleniyor...
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Selçuk Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
It 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.
Açıklama
Anahtar Kelimeler
Deep Features, Heart Failure, Long Short-Term Memory, Machine Learning Algorithms, Derin Özellikler, Kalp Yetmezliği, Uzun Kısa Süreli Bellek, Makine Öğrenme Algoritmaları
Kaynak
Selcuk University Journal of Engineering Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
23
Sayı
2
Künye
Acar, 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.
