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 ...
A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.
Abstract: Hypertension is a critical global health concern, necessitating accurate prediction models and effective prescription decisions to mitigate its risks. This study proposes a hybrid machine ...
Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...
1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...
Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...
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