A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.

The XGBoost model predicts hyperglycemia risk in psoriasis patients with high accuracy, achieving an AUC of 0.821 in the training set. A web-based calculator was developed to facilitate personalized ...

The SleepFM model reveals how sleep analysis can predict disease risk, offering insights into sleep's role as a vital health ...

Researchers worked with the Federal Reserve to create a predictive model that assesses hundreds of institutional ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology We used an innovative machine learning approach to analyze ...

AI model using deep transfer learning – the most advanced form of machine learning – predicted with 92 % accuracy spoken ...

A recent study published in npj Materials Degradation introduces a two-stage machine learning (ML) framework that predicts the degradation of protective coatings under various environmental conditions ...

Predicting earthquakes has long been an unattainable fantasy. Factors like odd animal behaviors that have historically been thought to forebode earthquakes are not supported by empirical evidence. As ...

To create their contactless approach, the researchers created a machine learning algorithm capable of analyzing aspects of ...