Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models
Cardiovascular disorders are the primary cause of death on a global scale. The World Health Organization report indicates that approximately 18 million people die from CVD each year. Major cardiac risks include arrhythmia and coronary artery disease, among others. Recent advancements in Artificial I...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10786989/ |
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| author | G. Sunilkumar P. Kumaresan |
| author_facet | G. Sunilkumar P. Kumaresan |
| author_sort | G. Sunilkumar |
| collection | DOAJ |
| description | Cardiovascular disorders are the primary cause of death on a global scale. The World Health Organization report indicates that approximately 18 million people die from CVD each year. Major cardiac risks include arrhythmia and coronary artery disease, among others. Recent advancements in Artificial Intelligence play a pivotal role for life-saving interventions in CVD treatment. This survey examines the latest progress in Machine Learning, Deep Learning and Pre-trained transfer learning models for classifying and predicting CVD using a survey of 159 articles, including 36 image datasets, 41 signal data and 52 clinical data from various sources. The survey investigates cardiac risk factors, cardiac dysfunction classification, various modalities and medical image processing techniques, performance metrics and hybrid techniques. Surveys on traditional neural networks such as Convolutional Neural Networks, Artificial Neural Networks, and Recurrent Neural Networks often achieve an accuracy rate of 70% to 95%. Leveraging pre-trained architectures such as ResNet, DenseNet, AlexNet, MobileNet, EfficientNet and GoogLeNet, transfer learning models consistently outperform other approaches frequently achieving accuracy levels greater than 96%. Researchers utilize various hybrid optimization algorithms to improve overall accuracy rate. The outcome of the survey supports a precise prognosis for patients with comorbidities. The survey findings indicate limitations in the incorporation of multimodal data for real-time risk assessment. The study results possess the capacity to bridge gaps in research on cardiovascular disease prediction, thereby assisting medical practitioners in early identification and subsequent prognosis. |
| format | Article |
| id | doaj-art-b66e384e81bf49a6ab74f363ba657a4e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b66e384e81bf49a6ab74f363ba657a4e2024-12-25T00:01:47ZengIEEEIEEE Access2169-35362024-01-011219336519338610.1109/ACCESS.2024.351409310786989Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction ModelsG. Sunilkumar0https://orcid.org/0009-0004-8270-2897P. Kumaresan1https://orcid.org/0000-0001-5563-8325School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, IndiaCardiovascular disorders are the primary cause of death on a global scale. The World Health Organization report indicates that approximately 18 million people die from CVD each year. Major cardiac risks include arrhythmia and coronary artery disease, among others. Recent advancements in Artificial Intelligence play a pivotal role for life-saving interventions in CVD treatment. This survey examines the latest progress in Machine Learning, Deep Learning and Pre-trained transfer learning models for classifying and predicting CVD using a survey of 159 articles, including 36 image datasets, 41 signal data and 52 clinical data from various sources. The survey investigates cardiac risk factors, cardiac dysfunction classification, various modalities and medical image processing techniques, performance metrics and hybrid techniques. Surveys on traditional neural networks such as Convolutional Neural Networks, Artificial Neural Networks, and Recurrent Neural Networks often achieve an accuracy rate of 70% to 95%. Leveraging pre-trained architectures such as ResNet, DenseNet, AlexNet, MobileNet, EfficientNet and GoogLeNet, transfer learning models consistently outperform other approaches frequently achieving accuracy levels greater than 96%. Researchers utilize various hybrid optimization algorithms to improve overall accuracy rate. The outcome of the survey supports a precise prognosis for patients with comorbidities. The survey findings indicate limitations in the incorporation of multimodal data for real-time risk assessment. The study results possess the capacity to bridge gaps in research on cardiovascular disease prediction, thereby assisting medical practitioners in early identification and subsequent prognosis.https://ieeexplore.ieee.org/document/10786989/Cardiovascular diseasemachine learningdeep CNNtransfer learningoptimization hybrid model |
| spellingShingle | G. Sunilkumar P. Kumaresan Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models IEEE Access Cardiovascular disease machine learning deep CNN transfer learning optimization hybrid model |
| title | Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models |
| title_full | Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models |
| title_fullStr | Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models |
| title_full_unstemmed | Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models |
| title_short | Deep Learning and Transfer Learning in Cardiology: A Review of Cardiovascular Disease Prediction Models |
| title_sort | deep learning and transfer learning in cardiology a review of cardiovascular disease prediction models |
| topic | Cardiovascular disease machine learning deep CNN transfer learning optimization hybrid model |
| url | https://ieeexplore.ieee.org/document/10786989/ |
| work_keys_str_mv | AT gsunilkumar deeplearningandtransferlearningincardiologyareviewofcardiovasculardiseasepredictionmodels AT pkumaresan deeplearningandtransferlearningincardiologyareviewofcardiovasculardiseasepredictionmodels |