The values of markers were transformed into log values before analysis using a multivariate classification algorithm.
Segmentations were generated by clustering those multi-variables using a multivariate classification method such as K-means cluster analysis [ 28].
The eight predictors were then evaluated simultaneously using a multivariate classification and regression tree (CART) model, which is a recursive binary partitioning model.
Differences in atrophy progression between PCA and tAD patients were subtle, reaching statistical significance only when using a multivariate classification approach.
The aims of the study were, first, to determine whether significant differences existed between cases and controls on cognitive tasks, and whether cognitive profiles, derived using a multivariate classification method with data from multiple cognitive tasks, could distinguish between the two groups.
To explore the possibility of reducing this delay, we used a multivariate pattern classification technique (linear support vector machine, SVM) to decode the true behavioral state from the measured neural signal and systematically evaluated the performance of different feature spaces (signal history, history gradient, oxygenated or deoxygenated hemoglobin signal and spatial pattern).
We used a multivariate analysis for validation of the forest classification, i.e. Polythetic Agglomerative Hierarchical Clustering (PAHC) [34] with the software PRIMER v.5.
Differences in FA values of white matter between OCD and healthy controls were examined using a multivariate pattern classification technique known as support vector machine (SVM).
Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) [ 18, 20] is a multivariate classification technique used for finding patterns in large multivariate data sets that describe differences between the groups under study.
A multivariate classification technique was used to examine whether depressive symptoms and symptoms frequently associated with depressive disorders would cluster into recognizable syndromes that parallel traditional DSM-III psychiatric diagnoses.
