IEEE

Introduction to clustering unsupervised machine learning algorithms applied to power quality disturbances

Abstract: Unsupervised machine learning algorithms, such as clustering and anomaly detection, work by identifying patterns and anomalies in data without the need for labeled training data. These ...
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 ...
IEEE

sQUlearn: A Python Library for Quantum Machine Learning [Focus: Quantum Software and Its Engineering]

Abstract: sQUlearn introduces a user-friendly, noisy intermediate-scale quantum (NISQ)-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine ...
BMJ

Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review

Background Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial ...