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.

翻译：心血管疾病（CVD）仍是全球范围内主要的致死原因，这要求开发更有效、更及时的检测方法。传统用于CVD检测的监督学习方法严重依赖大规模标注数据集，而此类数据通常难以获取。本文采用半监督学习模型，在标注样本稀缺的情况下提升CVD检测的效率和准确性。通过同时利用标注数据与大量未标注数据，我们的方法在降低对标注数据依赖的同时，提升了预测性能。在公开数据集上的实验结果表明，半监督模型优于传统监督学习技术，为临床环境中心血管疾病的早期识别提供了一种具有潜力的解决方案。