A comprehensive review of deep learning-based models for heart disease prediction

Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accu...
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Gespeichert in:

Autor*in:	Zhou, Chunjie [verfasserIn] Dai, Pengfei [verfasserIn] Hou, Aihua [verfasserIn] Zhang, Zhenxing [verfasserIn] Liu, Li [verfasserIn] Li, Ali [verfasserIn] Wang, Fusheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Heart disease Prediction Deep learning

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Artificial intelligence review - Springer Netherlands, 1986, 57(2024), 10 vom: 19. Aug.
Übergeordnetes Werk:	volume:57 ; year:2024 ; number:10 ; day:19 ; month:08

Links:	Volltext

DOI / URN:	10.1007/s10462-024-10899-9

Katalog-ID:	SPR057012334

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allfields	10.1007/s10462-024-10899-9 doi (DE-627)SPR057012334 (SPR)s10462-024-10899-9-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Zhou, Chunjie verfasserin aut A comprehensive review of deep learning-based models for heart disease prediction 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models. Heart disease (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dai, Pengfei verfasserin aut Hou, Aihua verfasserin aut Zhang, Zhenxing verfasserin aut Liu, Li verfasserin aut Li, Ali verfasserin aut Wang, Fusheng verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 10 vom: 19. Aug. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:10 day:19 month:08 https://dx.doi.org/10.1007/s10462-024-10899-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 10 19 08
spelling	10.1007/s10462-024-10899-9 doi (DE-627)SPR057012334 (SPR)s10462-024-10899-9-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Zhou, Chunjie verfasserin aut A comprehensive review of deep learning-based models for heart disease prediction 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models. Heart disease (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dai, Pengfei verfasserin aut Hou, Aihua verfasserin aut Zhang, Zhenxing verfasserin aut Liu, Li verfasserin aut Li, Ali verfasserin aut Wang, Fusheng verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 10 vom: 19. Aug. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:10 day:19 month:08 https://dx.doi.org/10.1007/s10462-024-10899-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 10 19 08
allfields_unstemmed	10.1007/s10462-024-10899-9 doi (DE-627)SPR057012334 (SPR)s10462-024-10899-9-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Zhou, Chunjie verfasserin aut A comprehensive review of deep learning-based models for heart disease prediction 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models. Heart disease (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dai, Pengfei verfasserin aut Hou, Aihua verfasserin aut Zhang, Zhenxing verfasserin aut Liu, Li verfasserin aut Li, Ali verfasserin aut Wang, Fusheng verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 10 vom: 19. Aug. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:10 day:19 month:08 https://dx.doi.org/10.1007/s10462-024-10899-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 10 19 08
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title_sort	a comprehensive review of deep learning-based models for heart disease prediction
title_auth	A comprehensive review of deep learning-based models for heart disease prediction
abstract	Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models. © The Author(s) 2024
abstractGer	Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models. © The Author(s) 2024
abstract_unstemmed	Abstract Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models. © The Author(s) 2024
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title_short	A comprehensive review of deep learning-based models for heart disease prediction
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Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)-related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of real-time HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. 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A comprehensive review of deep learning-based models for heart disease prediction

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