Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning
It addresses the challenge of obtaining large labeled datasets for CVD detection in clinical settings, offering a more practical approach, though it appears incremental.
This paper tackled the problem of cardiovascular disease detection by using semi-supervised learning to improve efficiency and accuracy with limited labeled data, showing that these models outperform traditional supervised techniques on a public dataset.
Cardiovascular disease (CVD) persists as a primary cause of death on a global scale, which requires more effective and timely detection methods. Traditional supervised learning approaches for CVD detection rely heavily on large-labeled datasets, which are often difficult to obtain. This paper employs semi-supervised learning models to boost efficiency and accuracy of CVD detection when there are few labeled samples. By leveraging both labeled and vast amounts of unlabeled data, our approach demonstrates improvements in prediction performance, while reducing the dependency on labeled data. Experimental results in a publicly available dataset show that semi-supervised models outperform traditional supervised learning techniques, providing an intriguing approach for the initial identification of cardiovascular disease within clinical environments.