Researchers employed a machine learning technique known as random forest analysis and found that it significantly outperformed traditional methods in predicting which hospitalized patients with ...

The disease is known for its strong association with climatic variables, especially excessive rainfall, high humidity, and ...

In the second part of the analysis, three machine learning models—Logistic Regression, Random Forest, and XGBoost—were implemented for predictive performance. Logistic Regression outperformed others ...

Using a dataset of 52,683 individuals aged 30 to 80, including both COVID-19 survivors and those unaffected, the study employs machine learning models—logistic regression, decision trees, and random ...

The study employs a Bayesian Optimization-enhanced Random Forest (BO_RF) algorithm for binary classification and a hybrid Logistic Regression and Random Forest (LR_RF) algorithm for multiclass ...

In this paper a brain tumor segmentation method is proposed which is based on the Random Forest algorithm. The proposed technique is applied to the brain magnetic resonance images and the performance ...

In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a ...

Keywords: machine learning, cardioembolism, large-artery atherosclerosis, small-artery occlusion, stroke Citation: Wang J, Gong X, Chen H, Zhong W, Chen Y, Zhou Y, Zhang W, He Y and Lou M (2022) ...