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 ...

The code isn’t the most illuminating aspect of Wall Street’s current AI sprint. It’s the atmosphere. Credit traders are half-listening to a risk presentation while scrolling through live pricing in ...

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

Monitoring and treating heart failure (HF) is a challenging condition at any age. Several models, such as Atrial fibrillation, Hemoglobin, Elderly, Abnormal renal parameters, Diabetes mellitus (AHEAD) ...

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

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

Researchers used 16S rRNA sequencing and machine learning to identify gut microbiome patterns associated with insulin resistance severity in people with type 2 diabetes. XGBoost models showed that ...

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 ...

ACGRIME is an improved metaheuristic algorithm derived from the original RIME framework. ACGRIME integrates three strategic mechanisms: chaotic initialization, adaptive weighting and Gaussian mutation ...

ABSTRACT: Accurate prediction of survey response rates is essential for optimizing survey design and ensuring high-quality data collection. Traditional methods often struggle to capture the complexity ...