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

A new research paper shows the approach performs significantly better than the random-walk forecasting method.

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...

The purpose is to comprehend the links between qualities, specifically any association between features and predicted labels. This includes detecting and fixing data problems (e.g. missing values, ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Abstract: As the lithography process continues to become more rigorous in advanced technology nodes, the model-based optical proximity correction (MBOPC), as a core component within computational ...

Abstract: In this work, the possibility of applying machine learning (ML) techniques to analyze and predict radio wave propagation losses in urban environments is explored. Thus, from a measurement ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

Background: Acute ST-segment elevation myocardial infarction (STEMI) is a cardiovascular emergency that is associated with a high risk of death. In this study, we developed explainable machine ...

This schematic illustrates the full workflow of a new study that integrates field and literature data, correlation analysis, and predictive modeling—including machine learning and geochemical ...