BMJ Open

Outcomes of open abdominal surgery at a single-centre tertiary referral hospital in Tanzania: a prospective, observational, single-arm cohort study

Objectives To assess the outcomes of patients undergoing open abdominal surgery at a National Referral Hospital in Tanzania. Design A prospective, observational, single-arm cohort study. Setting Dar ...
RMD Open

Therapist effects in real-world rehabilitation outcomes: a cohort study of the nationwide GLA:D osteoarthritis management programme in Denmark

Objective Unlike several other fields of healthcare, little is known about the size of ‘therapist effects’ on patient ...
Scientific Research Publishing

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features ()

Phishing is a form of cybercrime in which people are deceived into exposing their personal information which can result in ...
IEEE

An Advanced Hybrid Logistic Regression Model for Static and Dynamic Mixed Data Classification

Abstract: We consider the binary classification problem of static and dynamic mixed data in this paper. Different from mixed categorical and numerical data, the dynamic variables in the new type of ...
IEEE

Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection

Abstract: Because of the strong convexity and probabilistic underpinnings, logistic regression (LR) is widely used in many real-world applications. However, in many problems, such as bioinformatics, ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

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 ...
Frontiers

Expectation-maximization alternating least squares for tensor network logistic regression

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...
LinkedIn

Understanding the Sigmoid Function in Logistic Regression: Mapping Inputs to Probabilities

Logistic regression is a powerful statistical technique widely used in machine learning and statistics for binary classification problems. One of its key components is the sigmoid function, which ...
InfoWorld

The engines of AI: Machine learning algorithms explained

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...
Nature

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree ...
Visual Studio Magazine

Logistic Regression Using PyTorch with L-BFGS

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...