Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases

Table 4 Mean and standard deviation in AUC values (obtained via three-fold cross validation) for datasets S ₁, S ₂, and S ₃, while utilizing different DR-based multimodal data fusion methods (see Table 3 for details)

Strategy	Dataset S ₁	Dataset S ₂	Dataset S ₃
Non-imaging	0.774 ± 0.043	0.511 ± 0.078	0.771 ± 0.009
Imaging	0.885 ± 0.034	0.503 ± 0.076	0.564 ± 0.036
DFS-DD	0.905 ± 0.035	0.496 ± 0.079	0.752 ± 0.026
DFS-EC	0.675 ± 0.065^a	0.465 ± 0.111	0.720 ± 0.020
DFS-KC	0.888 ± 0.040	0.808 ± 0.067 ^b	0.857 ± 0.009 ^b
DFS-ES	0.789 ± 0.035	0.531 ± 0.086	0.748 ± 0.013

For baseline performance comparison, AUC values for the individual data modalities are also reported
^aindicates that the result was statistically significantly worse than comparative strategies
^bindicates that the result was statistically significantly better than comparative strategies
The best performing data fusion strategy for each classification task is highlighted in bold

ISSN: 1471-2342