For inpatients with cirrhosis, a machine learning (ML) model using random forest (RF) analysis is superior for prediction of inpatient mortality, according to a study published online July 23 in ...

A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

In this paper, we evaluate the effectiveness of the Random Forest algorithm in detecting intrusions within IoT environments through features selection analysis. We utilize the Network Intrusion ...

Apart from simple diagnosis, the study takes an important step toward predictive health monitoring by modeling the risk of ...

In this study, a diagnostic machine learning approach is proposed for automatic identification of laminar structures in sediments. The Yingxiongling shale oil in the Qaidam Basin of NorthWest China is ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

This study evaluated five machine learning algorithms random forest (RF), support vector machine (SVM), classification and regression trees (CARTs), gradient boosting trees (GBTs), and naïve Bayes for ...

Machine Learning Models Random Forest Random Forest is a versatile machine learning algorithm that is effective in handling large datasets with multiple features. By constructing a multitude of ...

This comprehensive approach enabled the accurate identification of zero-dose children, highlighting the effectiveness of machine learning in enhancing public health initiatives and optimising ...