Researchers have developed an advanced artificial intelligence (AI) framework designed to significantly improve the forecasting of carbon dioxide emissions in the aviation sector. ACGRIME is an ...

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

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 ...

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 ...

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

However, inconsistent travel times and unpredictable congestion continue to undermine service reliability, particularly in ...

A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve risk stratification.

"Sensing Intelligence and Machine Learning" describes the combination of artificial intelligence (AI) and machine learning (ML) approaches with sensor technologies. This fusion improves sensor ...

PanMETAI combines AI and NMR metabolomics to detect early-stage pancreatic cancer from a blood sample, achieving 93 percent ...

A publicly available AI tool correctly predicted approximately twice as many children with acute lymphoblastic leukemia who would relapse as three expert clinicians.XGBoost, a boosting algorithm, had ...