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 ...

Understanding how ozone behaves indoors is vital for assessing human health risks, as people spend most of their time inside.

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 ...

A machine learning random forest regression system predicts a single numeric value. A random forest is an ensemble (collection) of simple decision tree regressors that have been trained on different ...

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using comprehensive health examination data from nearly 37 701 individuals.Methods ...

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 ...

New research demonstrates that ML can also predict metre-scale laboratory earthquakes, suggesting that, when scaled, similar ...

AI is not limited to diagnostics or imaging. It also plays a transformative role in biomedical research, computational ...

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

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...