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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)

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

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 ± 0.065a 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
  1. For baseline performance comparison, AUC values for the individual data modalities are also reported
  2. aindicates that the result was statistically significantly worse than comparative strategies
  3. bindicates that the result was statistically significantly better than comparative strategies
  4. The best performing data fusion strategy for each classification task is highlighted in bold