A Review Of Cardiovascular Disease Prediction Models Using Deep Learning And Transfer Learning In Cardiology
Keywords:
Cardiovascular disease, machine learning, deep CNN, transfer learning, optimization hybrid, modelAbstract
Cardiovascular diseases (CVDs) represent the foremost cause of death on a global scale.According to a report by the World Health Organization, around 18.6 million individuals succumb to CVD annually. Key cardiac risks encompass arrhythmia and coronary artery disease, among others. Recent developments in Artificial Intelligence have become crucial for life-saving interventions in CVD treatment. This survey explores the latest advancements in Machine Learning, Deep Learning, and Pre-trained transfer learning models for classifying and predicting CVD, drawing on a review of 122 articles, which include 33 image datasets, 38 signal data, and 49 clinical data from diverse sources. The survey delves into risk factor of cardiovascular disease, cardiac impairment category, medical image processing techniques, performance metrics, and hybrid techniques. Studies on traditional neural networks like Convolutional Neural Networks, Artificial Neural Networks, and Recurrent Neural Networks often achieve accuracy rates ranging from 75% to 95%. By utilizing pre-trained architectures such as ResNet, DenseNet, Alex Net, Bi-GRU, Mobile Net, Efficient Net, and Google Net, BERT , transfer learning models consistently surpass other methods, frequently achieving accuracy levels exceeding 96%. Researchers employ various hybrid optimization algorithms to enhance the overall accuracy rate. The survey's findings support an accurate prognosis for patients with comorbidities The findings underscore challenges in combining multimodal data for real-time risk evaluation, while also offering valuable insights that could bridge existing gaps in cardiovascular disease prediction and support clinicians in early diagnosis and prognosis.
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