This article will cover the basic theory behind logistic regression, the types of logistic regression, when to use them and take you through a worked example.

EHR data may be particularly suitable for machine learning (ML) techniques, as such algorithms can process high-dimensional data and capture nonlinear relationships between variables. By comparison, ...

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams ...

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

An efficient algorithm is given for the exact logistic analysis of a single parameter. Hypothesis tests and confidence intervals for a logistic regression parameter are obtained. The method provides ...

As defined on TechTarget, logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a ...