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 ...

In this paper a novel nonlinear logistic regression model based on a simplex basis function neural network is introduced that outputs probability of categorical variables in response to multiple ...

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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 ...

Logistic Regression with KerasYou will see that linear Regression doesn’t perform well for the data points shown above because for x < 24, the model will predict class 1, hence making some errors as ...