A study published in Discover Artificial Intelligence used logistic regression, random forest and support vector machine (SVM ...

A team of researchers trained three machine learning models-Logistic Regression, Random Forest, and Support Vector Machine ...

Unlike most studies on healthcare workers' mental health, which focus on burnout and other negative outcomes, this research sought to predict a person's ability to actively cope with stress.

Three machine learning models trained to predict recurrent autoimmune hepatitis after liver transplantation were all outperformed by ordinary logistic regression, which reached an ...

Background Hospitalisations represent major clinical events in cirrhosis, yet prediction based on clinical variables alone ...

Materials scientists have long faced a stubborn trade-off: the very additives that make polymers resistant to fire often degrade their strength, conductivity, or durability. A new study published in ...

Machine learning predicted activated clotting time during AF ablation, with deep learning achieving 81% accuracy.

The researchers examined the Open University Learning Analytics Dataset, known as OULAD, and EdNet to determine which could support a meaningful picture of exploratory behavior.

Suppose a prediction model has an accuracy of 80%.By adding new data, using more advanced methods, and having experts spend time on it, it might be possible to raise it to 90%. An improvement of 10 ...

As heat waves, frosts and droughts increasingly batter the world’s wheat fields, a team of researchers in China has unveiled a deep learning model that can predict winter wheat yields with r ...

Hundreds of Bitcoin price models promise an edge, but research finds few can reliably beat simple forecasts out of sample.

Background Historical redlining, carried out by the Home Owners’ Loan Corporation (HOLC), assigned US neighbourhoods grades A ...