These differences may be due to study design (eg, identification of symptomatic disease or screening methods), treatment differences (eg, field weighing, shielding, use of anthracyclines), variable classification of valvular dysfunction, varying sample sizes, and recruitment bias as well as duration of follow-up.
In the case of the modified I2 grouping variable, classification was more successful in terms of correct classification percentages.
In addition, other limitations include suboptimal study design, heterogeneous and small-scale studies, inconsistent laboratory methods for HPV detection, variations in specimen retrieval and storage and variable histological classification of tumour tissue.
Analysis of variance (ANOVA) was used to identify predictors of psychosocial distress, including the categorical (non-ordinal) variable on classification of severity with ambiguity, paternal age, number of previous children, education, gestational age at inclusion, and time interval from suspicion of fetal anomaly to assessment.
Using Fourier and strut variables allows classification of subjects with 92% sensitivity, 96% specificity, and a 22% cross-validation error rate.
The importance of variables for classification of a single sample is provided by RF as the local importance.
Histological variables, including classification of histological subtypes, grade of nuclear atypia, and the presence of necrotic foci, were evaluated on slides stained with hematoxylin-eosin (HE).
One way, which proved successful in the NUSAP method, for reduction of assessment variables is a classification of input variables and a consequent sensitivity analysis to discern highly relevant input variables [25].
The method of logistic regression was used to further screen prognostic factors and recursive partitioning and classification tree for optimum discrimination of variables classification between good and poor recovery.
Two methods were used to model the response of a dependent variable (gullying) from a set of independent variables: Classification And Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS).
We further demonstrated that the performance of SLB forests is typically better than the performance of SLA forests with respect to the detection of discriminating variables, classification accuracy, and AUC.
