Cardiovascular disease is the leading cause of global mortality and responsible for millions
of deaths annually. The mortality rate and overall consequences of cardiac disease can be …

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million
mortalities worldwide. The early detection of heart failure with high accuracy is crucial for …

Heart disease is a significant global health issue, contributing to high morbidity and mortality
rates. Early and accurate heart disease prediction is crucial for effectively preventing and …

The present study examines the role of feature selection methods in optimizing machine
learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with …

Reinforcement learning is a powerful approach for predictive analysis to identify the weather
impacts for congenital heart disease. The key advantages of this method include the …

The complexity of cardiovascular disease onset emphasizes the vital role of early detection
in prevention. This study aims to enhance disease prediction accuracy using personal …

Prediction of heart diseases on time is significant in order to preserve life. Many
conventional methods have taken efforts on earlier prediction but faced with challenges of …

Introduction Diabetic retinopathy (DR) is the leading cause of preventable blindness in
Saudi Arabia. With a prevalence of up to 40% of patients with diabetes, DR constitutes a …

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading
cause of mortality, highlighting the need for improved predictive models. We introduce an …

The seamless communication between people and objects made possible by the Internet of
Things (IoT) greatly improves our quality of life. It is especially important in the remote …