openheart.bmj

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning

Background Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and benchmarking hospital performance. This study ...
Scientific Research Publishing

Cybersecurity and Forensic Audio Analysis: Deepfake Detection Based on Mfcc, Audio-Text Disconsistency, and Prosodic Features ()

1 Department of Computer and Instructional Technologies Education, Gazi Faculty of Education, Gazi University, Ankara, Türkiye. 2 Department of Forensic Informatics, Institute of Informatics, Gazi ...
IEEE

Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
psypost.org

Scientists use machine learning to control specific brain circuits

A team of researchers in Japan has developed an artificial intelligence tool called YORU that can identify specific animal behaviors in real time and immediately interact with the animals’ brain ...
The Lancet

When to and when not to use machine learning in risk prediction models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...
IEEE

Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study

Abstract: This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in ...