Despite medical advancements in recent years, cardiovascular diseases (CVDs) remain a
major factor in rising mortality rates, challenging predictions despite extensive expertise. The …

Enhancing cardiovascular disease prediction: A hybrid machine learning approach integrating
oversampling and adaptive boosting t Page 1 AIMS Medical Science, 11(2): 58–71. DOI …

In the medical domain, early identification of cardiovascular issues poses a significant
challenge. This study enhances heart disease prediction accuracy using machine learning …

Personalized interventions are deemed vital given the intricate characteristics,
advancement, inherent genetic composition, and diversity of cardiovascular diseases …

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating
accurate risk prediction for effective preventive measures. This comprehensive comparative …

Cardiovascular disease, refers to a variety of circumstances affecting the cardiovascular and
blood vessels, such as cardiovascular failure and coronary artery illness. Cardiovascular …

Background Current approaches to predict cardiovascular risk fail to identify many people
who would benefit from preventive treatment, while others receive unnecessary intervention …

This study compares three deep learning algorithms for cardiovascular disease risk
prediction. While RBFN boasts the highest accuracy (84.07%), wekaDeeplearning4j excels …

Cardiovascular disease (CVD) remains a primary contributor to global mortality,
necessitating refined predictive models. Machine learning (ML) has emerged as a pivotal …

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 …