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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