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Table 1 Comparison of classification results between HCs and dementia patients as well as AD and DLB using 10-fold cross-validation. Utilising a k-nearest neighbour machine learning model, in addition, we present the confidence interval (CI) for the specificity and sensitivity of each classification type

From: Investigating the power of eyes open resting state EEG for assisting in dementia diagnosis

Machine Learning classification accuracies

Classification Type

Accuracy

Specificity (± CI)

Sensitivity (± CI)

Weighted Average AUC

HC-D

0.91 ± 0.07

0.87 ± 0.09

0.92 ± 0.08

0.85

AD-LBD

0.86 ± 0.10

0.75 ± 0.19

0.9 ± 0.13

0.76

AD-DLB

0.82 ± 0.13

0.75 ± 0.19

0.81 ± 0.17

0.74

DLB-PDD

0.61 ± 0.16

0.76 ± 0.20

0.3 ± 0.22

0.61