Over a billion people worldwide have livers with excess fat, which can lead to a host of medical problems. Researchers think AI tools can spot the condition—and help stop it—early enough to save lives ...

A post hoc analysis of 2,970 older SELECT participants found that semaglutide slowed worsening of a 25-protein blood signature that predicts future dementia risk. Over 104 weeks, semaglutide produced ...

Artificial intelligence (AI)-based prediction models, including risk scoring systems and decision support systems, are being ...

Combining drone data and machine learning can help cover more ground in monitoring forest soil health, University of Alberta ...

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, ...

Researchers developed a two-stage machine learning framework that detected diabetes and classified records as prediabetes, type 1, type 2, or type 3c diabetes using two public datasets. XGBoost showed ...

Aims To develop and validate DeepAdapter, a novel deep learning algorithm that integrates self-supervised learning (SSL) and unsupervised domain adaptation (UDA) to enhance model generalisability for ...

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...