BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...

Abstract: Chronic Kidney Disease (CKD) remains a critical health issue on a global scale, often progressing undiagnosed until advanced stages. In this research, a machine learning (ML)-based ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

AI agents help businesses stop guessing — linking predictions to actions so teams can move from “what might happen” to ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

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 ...

Read more about AI-driven learning analytics struggle to deliver measurable gains in higher education on Devdiscourse ...

New research shows supervised machine learning models combining Helicobacter pylori genomic data with patient demographics can accurately predict gastric cancer risk.

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Researchers report redo heart surgery in adults with congenital heart disease remains high-risk, highlighting the need for a national, patient-specific risk model.