用少量标注数据实现更准的心脏病检测
Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning
- 结合少量标签数据与大量无标签数据提升模型性能
- 在公开数据集上比传统方法准确率更高
- 适合标注数据稀缺的临床诊断场景
心血管疾病（CVD）仍是全球主要死因之一，亟需更高效及时的检测手段。传统监督学习依赖大规模标注数据，而此类数据往往难以获取。本文采用半监督学习模型，在仅有少量标注样本的情况下，利用大量未标注数据提升心脏病检测的效率与准确性。实验结果表明，在公开数据集上，半监督模型优于传统监督学习方法，为临床环境中心血管疾病的早期识别提供了有前景的新路径。
原文摘要 · Abstract (English)
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.
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