Hybrid optimization enabled deep learning-based ensemble classification for heart disease detection

Heart diseases (HD) in humans are the most common cause of death. In the current global environment, the early detection of HD is a challenging process. The goal of this work is to develop a deep learning technique and to test the necessary classification model to improve HD detection. Hybrid optimi...

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Veröffentlicht in:	Signal, image and video processing Jg. 17; H. 8; S. 4235 - 4244
Hauptverfasser:	Jayasudha, R., Suragali, Chanti, Thirukrishna, J. T., Santhosh Kumar, B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.11.2023 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial intelligence Belief networks Cardiovascular disease Classification Computer Imaging Computer Science Computing time Data acquisition Data mining Datasets Decision making Deep learning Electrocardiography Ensemble learning Feature selection Heart Heart diseases Image Processing and Computer Vision Literature reviews Machine learning Medical diagnosis Methods Multimedia Information Systems Neural networks Optimization Optimization algorithms Original Paper Pattern Recognition and Graphics Signal,Image and Speech Processing Support vector machines Vision Wavelet transforms Driver training-based optimization Social water cycle driving training optimization Social optimization algorithm Deep belief network Ensemble classifier
ISSN:	1863-1703, 1863-1711
Online-Zugang:	Volltext
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