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The multivariate model was built using logistic regression.
In a learning data set, statistical prediction methods were built using logistic regression and a discrete event simulation approach for a one year prediction horizon.
Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage.
The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis.
As these models did not reject the null hypothesis, we cannot assert than they are significantly different from the model built using logistic regression and genetic algorithms in a wrapper approach.
A propensity model was built using logistic regression to model factors predictive of undergoing a non-clinical transfer, including patient and unit factors thought to be relevant on the basis of previous analyses.
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Models were built using logistic regressions to assess the predictive value of attitudes toward gender equality on adolescents' sexual behavior, on experiences and on communication.
The variables that were most statistically important (α = 0.05), base excess (BE) and platelets, were used to build a model using logistic regression, termed the BEP score.
Furthermore, we built a classification model using logistic regression to assess the performance of the purine pathway analytes in separating the groups in space.
Using logistic regression to build a model that maps performance parameters to the probability of failure.
The algorithm is implemented in three steps: Using logistic regression to build a model that maps performance parameters to the probability of failure.
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