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Table 3 Prediction accuracy of the neural network algorithm using the neuropsychological screening test dataset

From: Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

 

Prediction

Number of subjects

Accuracy of 10 trials

(mean ± SD%)

SE(%)

SP(%)

PPV(%)

NPV(%)

AUC

Balanced dataset

CI vs NC

3231: 3217

96.66 ± 0.52

96.0

96.8

97.0

95.8

0.964

MCI vs NC

3217: 3217

96.60 ± 0.45

96.0

97.4

97.6

95.6

0.967

ADD vs MCI vs NC

3235: 3217: 3217

95.49 ± 0.53

     

Clinic-based dataset

CI vs NC

11,709: 3217

97.23 ± 0.32

97.4

95.2

98.6

91.3

0.963

MCI vs NC

6002: 3217

97.05 ± 0.38

97.5

96.4

98.1

94.8

0.968

ADD vs MCI vs NC

5707: 6002: 3217

96.34 ± 1.03

     
  1. SD Standard deviation, SE Sensitivity, SP Specificity, PPV Positive predictive value, NPV Negative predictive value, AUC Area under the curve, CI Cognitive impairment, NC Normal cognition, MCI Mild cognitive impairment