The rise in stable coronary artery disease (CAD) due to improved survival rates and
population growth has increased patient numbers, straining healthcare systems. Machine …

Electronic health records (EHRs) are crucial for modern medical practices, providing digital
storage of patient health information. However, accurately identifying diseases that lead to …

The ability of the Extreme Gradient Boosting (XG-Boost) algorithm to classify subjects with
different type of breast cancer and those with and without heart disease is explored by …

Clinical narratives contain crucial patient information for predicting cardiac failure. Accurate
and timely cardiac failure recognition (CFR) significantly impacts patient outcomes but faces …

Cardiovascular diseases, such as Ischemic Heart Disease (IHD) and Dilated
Cardiomyopathy (DCM), collectively represent the leading cause of mortality worldwide. In …

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is
crucial to early recognize the disease subtypes and the unidentified clinical pathways that …

This paper aims to review the current advances in healthcare uses of machine learning (ML)
for the diagnosis of numerous diseases using different classification models. It proposes an …

Human Activity Recognition (HAR) plays a vital role in ubiquitous computing and has a wide
range of applications in healthcare, sports, and human-computer interaction. The study …

Breast cancer segmentation is a pivotal aspect of the diagnostic and treatment process for
effective breast cancer management. This paper introduces a novel approach to breast …

Heart failure stands as significant public health issue with the high mortality and morbidity
rates. Timely anticipation and detection of heart failure play a vital role in facilitating prompt …