Sentence examples for multivariate classification approaches from inspiring English sources

Multivariate classification approaches can be applied to small cohorts [25], [26].

Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning.

Accuracy for separating PCA and tAD groups using the same multivariate classification approach was lower (72.7%) but statistically significant.

We used the multivariate classification approach to predict al-EC and pm-EC subregions across all subjects.

These were created by repeating the multivariate classification approach, which reveals predictions for PRC and PHC preferential connectivity for each voxel, across all subjects.

Differences in atrophy progression between PCA and tAD patients were subtle, reaching statistical significance only when using a multivariate classification approach.

Furthermore, the fact that differences between PCA and tAD patients were sufficient to enable a multivariate classification approach to achieve statistically significant group separation may encourage further studies that assess longitudinal structural changes in larger subject groups.

We used the multivariate classification tree approach to investigate which of the fitted model parameters differed significantly between the study communities and which may, therefore, be considered to underlie any between-community variation in the model outputs [ 47].

Here, we compared fMRI results from orientation-specific visual adaptation and orientation classification by MVPA, using optimized experimental designs for each, and found that the multivariate pattern classification approach was more sensitive to small differences in stimulus orientation than the adaptation paradigm.

For this reason the simultaneous quantification of multiple biomarkers, that may include demographic/risk-factor(s), cervical length and biochemical marker(s), and the development of multivariate classification models represent a promising approach to improving diagnostic efficiency.

The solution discussed also touches on the use of flexible analytic approaches, such as multivariate classification.