Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The
relationship between external risk factors and our genetics have not been well established. It …

Background Disease progression in patients with mild-to-moderate aortic stenosis is
heterogenous and requires periodic echocardiographic examinations to evaluate severity …

Artificial Intelligence is extensively applied in heartcare to analyze patient data, detect
anomalies, and provide personalized treatment recommendations, ultimately improving …

Background and objective Diabetic retinopathy (DR) is a global health concern among
diabetic patients. The objective of this study was to propose an explainable machine …

To achieve risk assessment of atherosclerotic cardiovascular disease, the number of
features directly affects the performance of the model. In this paper, we propose a L1 …

Background: Supporting and understanding the health of patients with chronic diseases and
cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in …

Background Electronic medical records (EMR)-trained machine learning models have the
potential in CVD risk prediction by integrating a range of medical data from patients, facilitate …

Cardiovascular disorders are the primary cause of death on a global scale. The World
Health Organization report indicates that approximately 18 million people die from CVD …

Cardiovascular disease (CVD) is the leading cause of death globally, demanding accurate
risk prediction models for early intervention and prevention. This project aimed to develop a …

Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and it is of utmost
importance to accurately assess the risk of cardiovascular disease for prevention and …