A comparative summary of relevant work on cardiovascular disease prediction using machine learning and deep learning. The table compares methods, datasets, key performance metrics, and key findings. This review highlights the major issues in the area, such as the interpretability, the generalizability, the cost of computation, and the privacy of data, all of which motivate the proposed hybrid model.
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https://figshare.com/articles/dataset/A_comparative_summary_of_relevant_work_on_cardiovascular_disease_prediction_using_machine_learning_and_deep_learning_The_table_compares_methods_datasets_key_performance_metrics_and_key_findings_This_review_highlights_the_major_issues_in_the/30482407
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资源简介：
A comparative summary of relevant work on cardiovascular disease prediction using machine learning and deep learning. The table compares methods, datasets, key performance metrics, and key findings. This review highlights the major issues in the area, such as the interpretability, the generalizability, the cost of computation, and the privacy of data, all of which motivate the proposed hybrid model.
创建时间：
2025-10-29
