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 ...

Widely used method underestimates forests' ability to prevent major floods, researchers argue

Researchers from the University of British Columbia argue that a widely used method to understand and predict flood risk has led scientists to miscalculate how forests can prevent major flooding. The ...
SciELO

Analysis of Factors Affecting Egg Consumer Prices in Türkiye Using Random Forest Regression

Despite the substantial increase in egg production and consumption in Türkiye in recent years, price fluctuations remain prevalent. Forecasting consumer prices for eggs is, therefore, a complex ...
IEEE

An Improved Iterative Learning Control Approach Based on Random Forest and Whale Optimization for Urban Traffic Signals

Abstract: Iterative learning control (ILC) has demonstrated effectiveness in urban traffic signal control systems. However, conventional ILC methods typically require infinite iterations to achieve ...
GitHub

Greekkgod/Titanic-Survival-Prediction-using-Random-Forest-Regression

This project is built to predict passenger survival on the Titanic using supervised machine learning models. It follows a complete ML pipeline from data exploration to model evaluation.
Scientific Research Publishing

He, S., Wu, J., Wang, D., & He, X. (2022). Predictive Modeling of Groundwater Nitrate Pollution and Evaluating Its Main Impact Factors Using Random Forest. Chemosphere, 290 ...

ABSTRACT: An integrated model approaching to combining the BETR-GLOBAL model with a Random Forest method was developed in this research. Firstly, the BETR-GLOBAL model was employed to simulate the ...