Risk prediction of cardiac death following percutaneous coronary intervention remains suboptimal in acute myocardial infarction. This study aimed to develop and externally validate an interpretable ...

Six machine learning algorithms—k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, support vector machine, and Extreme Gradient Boosting (XGBoost)—were developed using 10-fold ...

Introduction Angina with no obstructive coronary artery disease (ANOCA) affects millions and is frequently under-recognised because diagnostic pathways and risk tools predominantly target obstructive ...

Smart Prevention and Precision Care: Machine Learning in Cardiometabolic and Oncologic Diseases ...

Objective: To investigate the prognostic value of postoperative radiotherapy (RT) for overall survival (OS) in patients with de novo metastatic hormone receptor (HR)-positive breast cancer, and to ...

Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

VIJAYAWADA: Venkata Sree Karthikeya Gattupalli, a tech entrepreneur with roots in Tadepalli, has been selected to present his ...

Researchers have for the first time virtually unwrapped and read an entire sealed Herculaneum papyrus scroll, carbonized ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...