A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a complication that can occur late in pregnancy.
WPI researchers have used a form of artificial intelligence (AI) to analyze anatomical changes in the brain and predict ...
Objectives To evaluate whether type 2 diabetes mellitus (T2DM) presence and severity are associated with differences in global and domain-specific cognitive function among US adults, using ...
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 ...
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 ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
ABSTRACT: Educational research stands at a crossroads that is both methodological and philosophical. The field must decide whether to remain anchored in a toolkit built for small samples and linear ...
MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results