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.

Background Cardiovascular disease (CVD) is the leading cause of mortality worldwide, while depression is highly prevalent in this patient population and has long been regarded as an independent risk ...

Quadratic regression extends linear regression by adding squared terms and pairwise interaction terms, enabling the model to capture non-linear structure and predictor interactions. The article ...

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...

A complete implementation of Logistic Regression with Gradient Descent optimization from scratch using only NumPy, demonstrating mathematical foundations of binary classification for diabetes ...

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 goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Doing logistic regression with a binary outcome using the Generalized Linear Model analysis in Regression module should work. This works fine in Regression > Logistic Regression.

Participants were recruited from four geographically diverse provinces (Eastern: Shandong; Western: Shaanxi, Sichuan; Southern: Hainan) across 8 universities (5 comprehensive universities, 3 ...