A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Machine learning predicts who will decline faster in Alzheimer’s disease using routine clinic data
Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
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 ...
Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?
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Physical function metrics improve mortality prediction in elderly heart failure patients
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 ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Insulin resistance-when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels-is ...
Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...
Studying gene expression in a cancer patient's cells can help clinical biologists understand the cancer's origin and predict the success of different treatments. But cells are complex and contain many ...
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