Background: Race is a social construct reflecting broader systemic forces that can affect health, including mental health. We ...

Objectives This study uses nationally representative survey data from the USA to estimate the relationship between a history of heart attack or stroke with the prevalence of mental health symptoms.

Objectives We aimed to understand the factors affecting psychological well-being of patients undergoing haemodialysis (HD). First, we explored how physical symptom severity, emotional distress and ...

The cumulative logistic regression model, also known as the proportional odds model (POM), is commonly used for analyzing ordinal data because of its effectiveness in providing generalizing ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

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This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and ...

Traditional logistic regression analysis is widely used in the binary classification problem, but it has many iterations and it takes a long time to train large amounts of data, which is not ...

Logistic Regression Model Optimization and Case Analysis Traditional logistic regression analysis is widely used in the binary classification problem, but it has many iterations and it takes a long ...

There are dozens of code libraries and tools that can create a logistic regression prediction model, including Keras, scikit-learn, Weka and PyTorch. When training a logistic regression model, there ...