Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

AI & Society, states that algorithmic systems often construct competing but equally valid “model-worlds,” offering empirical support for a philosophical claim that evidence alone cannot uniquely ...

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

AI compresses the decision-action loop, turning hours of analytical deliberation into seconds of machine-assisted judgment. In warfare, that compression is the difference between survival and defeat.

How do Non-Human Identities Shape the Future of Cloud Security? Have you ever wondered how machine identities influence cloud security? Non-Human Identities (NHIs) are crucial for maintaining robust ...

A new analysis of gene expression in blood samples suggests that specific biological signs of Parkinson’s disease are detectable years before physical symptoms appear. These molecular signatures, ...