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

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

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 ...

Insulin resistance - when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels - ...

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?

Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may underestimate risk in elderly East Asian patients. Researchers from Japan used ...

Insulin resistance-when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels-is ...

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

Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...

A new study suggests that an AI-assisted blood test, which measures for biomarkers, could help to identify prediabetes risk earlier.