Logistic regression is preferrable over a simpler statistical test such as chi-squared test or Fisher’s exact test as it can incorporate more than one explanatory variable and deals with possible ...

Binary Logistic Regression: Binary logistic regression is employed when the dependent variable has only two outcomes—in this case, the dependent variable is referred to as a dichotomous variable.

Logistic regression calculated ORs with 95% CI for the likelihood of pathological response adjusting for ... Directed acyclic graph depicting potential causal pathways for any association between the ...

Study: From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Image Credit: Have a nice day Photo / Shutterstock.com. Common ML models in ...

Logistic regression is another commonly used type of regression. This is where the outcome (dependent) variable takes a binary form (where the values can be either 1 or 0). Many outcome variables take ...

We developed a modified logistic-regression model for lung-cancer prediction in the PLCO control group of smokers. This model was referred to as PLCO M2012 to distinguish it from its predecessor ...

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams. JCO Clin Cancer Inform 6 , e2200039 (2022). DOI: ...