A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
This is the largest real-world analysis of mycophenolic acid in pediatric lupus nephritis to date, providing a decision-support system to help balance efficacy and safety.
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 ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
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 ...
A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.
Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
Domain adaptation may be a novel creative solution to predict infection risk in patients with chronic lymphocytic leukemia ...
News Medical on MSN
Machine learning detects early brain changes linked to Alzheimer's disease
Worcester Polytechnic Institute (WPI) researchers have used a form of artificial intelligence (AI) to analyze anatomical changes in the brain and predict Alzheimer's disease with nearly 93% accuracy.
Rapid advances in artificial intelligence, machine learning, and data-driven computational modeling have opened unprecedented opportunities to transform ...
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