Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Discover how random forests, a machine-learning technique, enhance prediction accuracy by combining insights from multiple ...

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...

This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...