Table 3 Performance of the deep learning models. The long short-term memory (LSTM) network and the convolutional neural network (CNN) models were constructed to classify participants with normal cognition and dementia as well as participants who are non-demented and the ones with dementia, respectively. On each model, a 5-fold cross-validation was performed and the model predictions (mean ± standard deviation) were generated on the test data (see Figure S1). A and B report the performances of the LSTM and the CNN models for the classification of participants with normal cognition versus those with dementia. C and D report the performances of the LSTM and the CNN models for the classification of participants who are non-demented versus those who have dementia

Model

LSTM-5 min

LSTM-10 min

LSTM-15 min

LSTM-full audio

 Accuracy

0.581 ± 0.039

0.578 ± 0.037

0.593 ± 0.051

0.598 ± 0.035

 Balanced accuracy

0.642 ± 0.029

0.641 ± 0.027

0.650 ± 0.035

0.647 ± 0.027

 Sensitivity

0.420 ± 0.065

0.412 ± 0.067

0.442 ± 0.093

0.470 ± 0.077

 Specificity

0.865 ± 0.022

0.871 ± 0.031

0.859 ± 0.034

0.824 ± 0.025

 Precision

0.844 ± 0.019

0.849 ± 0.029

0.846 ± 0.025

0.824 ± 0.010

 F1 score

0.558 ± 0.061

0.551 ± 0.062

0.575 ± 0.083

0.595 ± 0.063

 Weighted F1 score

0.573 ± 0.046

0.569 ± 0.046

0.586 ± 0.061

0.596 ± 0.047

 MCC

0.294 ± 0.050

0.294 ± 0.049

0.307 ± 0.060

0.294 ± 0.046

 Precision-recall AUC

0.814 ± 0.016

0.819 ± 0.020

0.803 ± 0.029

0.805 ± 0.022

 ROC AUC

0.742 ± 0.017

0.745 ± 0.011

0.737 ± 0.020

0.740 ± 0.017

Model

CNN-5 min

CNN-10 min

CNN-15 min

CNN-full audio

 Accuracy

0.666 ± 0.035

0.674 ± 0.052

0.710 ± 0.021

0.740 ± 0.033

 Balanced accuracy

0.587 ± 0.054

0.650 ± 0.035

0.698 ± 0.015

0.743 ± 0.015

 Sensitivity

0.873 ± 0.079

0.738 ± 0.118

0.740 ± 0.045

0.735 ± 0.094

 Specificity

0.300 ± 0.160

0.562 ± 0.095

0.656 ± 0.038

0.750 ± 0.083

 Precision

0.691 ± 0.036

0.750 ± 0.025

0.792 ± 0.013

0.844 ± 0.034

 F1 score

0.769 ± 0.028

0.738 ± 0.064

0.765 ± 0.023

0.780 ± 0.048

 Weighted F1 score

0.623 ± 0.061

0.672 ± 0.047

0.712 ± 0.019

0.742 ± 0.033

 MCC

0.207 ± 0.106

0.308 ± 0.077

0.389 ± 0.034

0.477 ± 0.026

 Precision-recall AUC

0.743 ± 0.038

0.801 ± 0.024

0.837 ± 0.012

0.876 ± 0.028

 ROC AUC

0.640 ± 0.054

0.716 ± 0.038

0.759 ± 0.019

0.805 ± 0.027

Model

LSTM-5 min

LSTM-10 min

LSTM-15 min

LSTM-full audio

 Accuracy

0.651 ± 0.016

0.659 ± 0.022

0.648 ± 0.023

0.675 ± 0.013

 Balanced accuracy

0.651 ± 0.016

0.659 ± 0.022

0.648 ± 0.023

0.675 ± 0.013

 Sensitivity

0.576 ± 0.048

0.565 ± 0.062

0.556 ± 0.059

0.578 ± 0.049

 Specificity

0.726 ± 0.031

0.753 ± 0.024

0.740 ± 0.035

0.772 ± 0.027

 Precision

0.677 ± 0.016

0.694 ± 0.012

0.680 ± 0.025

0.716 ± 0.011

 F1 score

0.621 ± 0.027

0.621 ± 0.040

0.610 ± 0.038

0.638 ± 0.028

 Weighted F1 score

0.649 ± 0.016

0.655 ± 0.024

0.644 ± 0.025

0.671 ± 0.015

 MCC

0.306 ± 0.031

0.324 ± 0.040

0.302 ± 0.046

0.357 ± 0.022

 Precision-recall AUC

0.685 ± 0.012

0.682 ± 0.019

0.670 ± 0.025

0.701 ± 0.016

 ROC AUC

0.720 ± 0.013

0.726 ± 0.009

0.711 ± 0.019

0.734 ± 0.014

Model

CNN-5 min

CNN-10 min

CNN-15 min

CNN-full audio

 Accuracy

0.555 ± 0.022

0.624 ± 0.030

0.628 ± 0.042

0.653 ± 0.020

 Balanced accuracy

0.555 ± 0.023

0.623 ± 0.030

0.627 ± 0.042

0.652 ± 0.020

 Sensitivity

0.663 ± 0.224

0.546 ± 0.101

0.486 ± 0.076

0.457 ± 0.106

 Specificity

0.447 ± 0.188

0.701 ± 0.065

0.769 ± 0.038

0.847 ± 0.068

 Precision

0.543 ± 0.011

0.646 ± 0.034

0.674 ± 0.053

0.760 ± 0.049

 F1 score

0.576 ± 0.120

0.587 ± 0.055

0.563 ± 0.063

0.560 ± 0.068

 Weighted F1 score

0.528 ± 0.035

0.619 ± 0.030

0.619 ± 0.045

0.635 ± 0.031

 MCC

0.128 ± 0.055

0.253 ± 0.062

0.265 ± 0.085

0.337 ± 0.024

 Precision-recall AUC

0.597 ± 0.041

0.643 ± 0.033

0.655 ± 0.044

0.732 ± 0.015

 ROC AUC

0.595 ± 0.043

0.663 ± 0.033

0.683 ± 0.037

0.746 ± 0.021