Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...

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 ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

[Hello, I would like to contribute to the QuickFactChecker project. I have prepared a comparison of baseline models for the LIAR dataset, including Naive Bayes, Logistic Regression, and Random Forest, ...

Department of General Practice, The Affiliated Hospital of Qingdao University, Qingdao, China Objective: To identify risk factors for hypoglycemia in hospitalized patients with type 2 diabetes ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

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 ...

Recently, Aircela, a fuel company headquartered in New York, publicly demonstrated a machine in Manhattan that produces gasoline directly from air. The event attracted city and state officials, ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...