Open Heart

Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure

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 ...
Scientific Research Publishing

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

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

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

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

Future Directions in Electric Load Forecasting: Leveraging Random Forest for Improved Accuracy

Abstract: With the advancement of power technologies in the modern era, electric load forecasting has become increasingly crucial in domains including power grid layout optimization, electricity ...
Phys.org

Wildfire prevention models miss key factor: How forests will change over decades

Eucalyptus trees, laden with flammable oils, could spread into Portugal's south-central region by 2060 if changing climate conditions make the area more hospitable to their growth, creating wildfire ...
IEEE

Application of Support Vector Regression and Random Forest Methods in Predicting Air Quality Index (AQI) Concentration in Jakarta

Abstract: Air pollution is a significant global challenge that profoundly impacts both the environment and public health. Accurate prediction of the Air Quality Index (AQI) is essential for promoting ...