Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning

TL;DR

This paper introduces a semi-supervised learning approach for cardiovascular disease detection that improves accuracy and reduces reliance on labeled data by leveraging both labeled and unlabeled datasets, showing promising results in clinical data.

Contribution

It presents a novel semi-supervised learning framework for CVD detection that outperforms traditional supervised methods with less labeled data.

Findings

Semi-supervised models outperform supervised techniques in prediction accuracy.

Reduced need for labeled data improves efficiency in clinical settings.

Experimental results validate the effectiveness of the approach.

Abstract

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.