Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference

Stavroula C. Tassi, Konstantinos D. Polyzos

Keywords: AI for Health, CVD inference, Gaussian Processes (GPs), Ensemble of Gaussian Processes (EGPs), FFR index

Abstract:

Cardiovascular disease (CVD) poses a significant global health challenge, and accurate inference methods are vital for early detection and intervention. However, the quality of prediction relies heavily on the availability of labeled data, which are often limited in medical applications. To cope with the challenge of limited labeled data, we are the first to propose an active learning (AL) approach that leverages a weighted ensemble of Gaussian processes to effectively infer CVD by strategically selecting the few most informative data points to label. Through experiments conducted on the SMARTool dataset, we demonstrate the effectiveness of the advocated approach, achieving superior performance in CVD inference compared to baseline methods. Our findings highlight the potential impact of the proposed AL framework in CVD diagnosis and treatment clinical cases, particularly in scenarios where labeled data are scarce, due to data onfidentiality concerns or high sampling costs.

Reference of accepted paper: The paper is a conference paper at accepted at the IEEE International Conference on Bioinformatics and Biomedicine. The link: https://ieeexplore.ieee.org/abstract/document/10385861

Submission Number: 126