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Each sub-model of the ensemble was developed using a forward selection procedure.
A multivariable model was developed using a forward stepwise approach.
A multivariable model was developed using a forward stepwise based approach.
The prediction models were developed using a forward selection method using a threshold p-value of 0.10 for variables to be included in the models.
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A logistic regression model was developed using a stepwise forward strategy.
A main-effect logistic-regression model was developed using a stepwise forward selection process to identify baseline factors predictive of SRI response at week 52 irrespective of the treatment received.
An unconditional logistic regression model will be developed using a stepwise forward selection approach (criteria for entry p < 0.05 and removal p < 0.1), to identify risk factors for pneumonia incorporating adjustment for potential confounders as well as matching factors (matching factors will be treated as nuisance parameters and their coefficients will not be reported).
The RNN_WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm.
The RSM model was developed using Box-Behnken design and the ANN predictive model was developed using a feed-forward backpropagation neural network algorithm with 14 neurons in the hidden layer.
The ANN predictive models of surface roughness was developed using a multilayer feed forward neural network and trained with the help of an error back propagation learning algorithm based on the generalized delta rule.
The models were developed using a supervised feed-forward Artificial Neural Network (ANN) architecture to account for the effect of inherent nonlinearity in the relationships between driving behavior and secondary tasks.
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