Table 3 summarises the main techniques used to target each predictive variable.
As described, using a point scale from 0 to 100, each predictive variable is 'weighted' by assigning a point score.
There are several reasons why adding a new predictive variable may not result in a better accuracy of a predictive model including a low prevalence of the variable, the absence of predictive value of this variable, and the presence of a correlation between the predictive variable and the variable(s) originally included in the predictive model (i.e. APACHE-II).
The association between each predictive or postoperative variable and patient group was investigated analysing the corresponding contingency table.
Effect size, i.e. the strength of the association between a significant predictive variable and the dependent variable, was calculated as η = SSeffect/SStotal, where η is the proportion of the effect variance (SSeffect) to the total variance (SStotal).
In each model, the multicollinearity was investigated between each variable: the variable that led to the best predictive model was retained.
This predicted distribution is obtained by taking into account non-linear interactions between the predictive variables to evaluate the similarity between the instances of the dataset.
Clinical research examining the relationship between the predictive variables for neurologic adverse events and cerebral blood flow has suggested some surgical strategies for minimizing risk, such as limiting manipulation of the ascending aorta.
We further examined the data for any association between possible predictive variables, e.g., demographic data, history of aspirin sensitivity, asthma, allergy skin test and sinusitis, and therapeutic response to this dual modality.
The 95% CIs for each variable were used to determine the relationships between the various predictive variables and outcomes.
