In materials science, substances are often classified based on defining factors such as their elemental composition or ...

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

To address this issue, the researchers applied the Random Forest machine learning model, which enables the simulation of missing or unmeasured geochemical elements.

Ensemble models, comprising multiple algorithms or simulations to improve prediction accuracy, are increasingly being applied ...

Their proof-of-concept employs a random forest machine learning classifier to interpret image feature variations on RAT NC membrane, correlating red blood cell (RBC) wicked diffusion distance in ...

This study contributes to the literature on union dissolution by adopting a machine learning (ML) approach, specifically Random Survival Forests (RSF). We used RSF to analyze data on 2,038 married or ...

Researchers used four techniques to identify potential IBD biomarkers in the samples: Differential abundance analysis, supervised random forest machine learning, unsupervised network analysis, and ...