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Table 2 Performance of the ANNs in discriminating AD cases from normal controls. The analysis was carried out on all 4 neuropathologic variables registered in the original database of patients in ten separated experiments with different training and testing subsets. Linear Discriminant Analysis [LDA] results on the same subsets are shown for comparison.

From: Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

Tr and Ts subsets ANN LDA
  AD Normal Mean accuracy AD Normal Mean accuracy
FF_Bp*(4 × 2)1a 100.00% 100.00% 100.00% 100.00% 87.50% 93.75%
FF_Bp(4 × 2)1b 100.00% 100.00% 100.00% 100.00% 91.67% 95.83%
FF_Bp(4 × 2)2a 100.00% 100.00% 100.00% 100.00% 72.73% 86.36%
FF_Bp(4 × 2)2b 100.00% 100.00% 100.00% 100.00% 88.89% 94.44%
FF_Bp(4 × 2)3a 100.00% 100.00% 100.00% 100.00% 87.50% 93.75%
FF_Bp(4 × 2)3b 100.00% 100.00% 100.00% 100.00% 83.33% 91.67%
FF_Bp(4 × 2)4a 100.00% 100.00% 100.00% 100.00% 72.73% 86.36%
FF_Bp(4 × 2)4b 100.00% 100.00% 100.00% 95.00% 100.00% 97.50%
FF_Bp(4 × 2)5a 100.00% 100.00% 100.00% 100.00% 91.67% 95.83%
FF_Bp(4 × 2)5b 100.00% 100.00% 100.00% 100.00% 75.00% 87.50%
Average 100.00% 100.00% 100.00% 99.50% 85.10% 92.30%
  1. * Feed Forward Back Propagation Neural Network
  2. Tr: Training set; TS: Testing set