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 ...
The researchers argue that their findings, published in Scientific Reports, could help clinicians anticipate which patients ...
Understanding how ozone behaves indoors is vital for assessing human health risks, as people spend most of their time inside.
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 ...
Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...
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 ...
Machine learning models delivered the strongest performance across nearly all evaluation metrics. CHAID and CART provided the ...
It’s been discovered that a new tool using routine blood tests and a simple online app could help detect tuberculosis (TB) ...
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