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When predictive coefficients from this population were used to classify a different group of 206 ambulatory adults, past utilization also increased in stepwise order by case-mix class.
This limited polyUb chain synthesis occurred nonprocessively; i.e., reaction products appear in stepwise order and subsequently disappear in conjunction with the appearance of the next slower-migrating band.
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The potential confounders were added to the model in a stepwise order according to their effect on the risk estimate but were kept in the model only if the exclusion changed risk estimate at least 5%.
This ranking can be used to build a predictive model, without eliminating any variables, using any other machine learning prediction method, in this case and differently from Genuer et al [43], Support Vector Machines [48], inserting the variables stepwise in order to find a good balance between the number of variables and prediction error.
Variables entered stepwise in order: Gender + Age+ Patient status.
Variables that showed significant association with the outcome were included in the multivariate Cox proportional-hazard model in a stepwise method, in order to determine the independent predictors for morbidity and oncological outcome.
Currently, most organic synthesis is carried out in stepwise processes.
Subsequently, we removed predictors with non-significant effects stepwise, in reversed order of significance, until only significant effects remained (alpha = 5%%).
Factors were then removed stepwise in descending order of P-values whenever they were found to be non-significant (P < 0.05), yielding the final model.
This model was created by adding these predictors stepwise in descending order of individual impact on decision satisfaction (determined by the change in −2log likelihood in their separate models representing the quantity of improvement of model fit).
Therefore a stepwise approach was combined with GA in order to reach local convergence as it is quick and adapted to find solutions in "promising" areas already identified.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com