For example, tools like the EuroSCORE and ACS NSQIP ... These variables were integrated into an initial model, and a multivariable logistic regression was performed, with a purposeful backward ...

Objective To develop a multivariable model for predicting the progression of systemic sclerosis-associated interstitial lung disease (SSc-ILD) over 52 weeks. Methods We used logistic regression models ...

Then, we further used a multivariate logistic regression model for final variable screening ... at the bottom of the chart to get an estimated probability of risk. For example, a 50-year-old patient ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

Multivariable logistic regression was used in the primary analyses of the propensity-weighted population to evaluate the relationship between minimally invasive surgery and in-hospital mortality. Our ...

Abstract: Rain prediction is challenging due to the complex combination of atmospheric factors. This paper presents the application of logistic regression modelling to predict rainfall the next day, ...