Frontiers

Integrating phase-rectified signal averaging with machine learning to predict stroke-associated infections: a retrospective cohort study

Background: Stroke-associated infection (SAI) adversely affects the prognosis of acute ischemic stroke (AIS) patients, contributing to poorer functional outcomes and survival. The absence of validated ...
IEEE

Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs

Abstract: In Software Quality Assurance (SQA), predicting defect-prone software modules is essential for ensuring software reliability and consistency. This task is commonly achieved through Machine ...
Frontiers

Development of a machine learning-based risk prediction model for early-stage pneumoconiosis: a retrospective study

Background: The diagnosis of occupational pneumoconiosis requires more accurate predictive models. The purpose of this study is to screen blood markers associated with early pneumoconiosis development ...
IEEE

Performance Analysis of Machine Learning Models for Predicting Return Loss in 5G Microstrip Patch Array Antenna Design

Abstract: This work explores the integration of machine learning models to predict the return loss of a microstrip antenna operating at 3.5 GHz in the sub-6 GHz 5G frequency band. 11 (eleven) design ...