Table 4 Comparison of the performance of classifiers using different combinations of indicators

From: Multimodal neuroimaging-based prediction of Parkinson’s disease with mild cognitive impairment using machine learning technique

Classifiers

Features

ACC

AUC

Sensitivity

Specificity

clinical

T1

Rs-fMRI

NfL

GFAP

1

×

×

0.762

0.840

0.745

0.783

2

×

×

0.759

0.834

0.783

0.736

3

×

×

×

0.758

0.832

0.781

0.739

4

×

0.756

0.830

0.706

0.813

5

×

0.749

0.830

0.705

0.798

6

×

0.748

0.830

0.780

0.719

7

×

×

0.749

0.828

0.769

0.809

8

×

×

0.746

0.825

0.681

0.784

9

0.741

0.824

0.697

0.775

10

×

×

×

0.753

0.822

0.735

0.771

11

×

0.739

0.821

0.678

0.784

12

×

×

0.755

0.819

0.667

0.842

13

×

×

0.746

0.818

0.765

0.707

14

×

×

×

0.716

0.790

0.648

0.794

15

×

×

0.716

0.790

0.648

0.794

16

×

×

×

×

0.651

0.790

0.651

0.831

17

×

×

×

0.748

0.773

0.647

0.833

18

×

×

0.665

0.739

0.610

0.736

19

×

×

0.644

0.739

0.691

0.614

20

×

×

×

0.671

0.737

0.605

0.750

21

×

0.655

0.735

0.590

0.735

22

×

×

×

0.700

0.731

0.589

0.824

23

×

×

0.668

0.729

0.606

0.743

24

×

×

×

0.657

0.724

0.669

0.656

25

×

×

×

0.650

0.710

0.691

0.623

26

×

×

×

0.613

0.670

0.621

0.623

27

×

×

×

×

0.641

0.651

0.512

0.784

28

×

×

×

×

0.513

0.499

0.621

0.449

29

×

×

×

0.457

0.494

0.545

0.422

  1. ACC accuracy, AUC area under curve, Rs-fMRI resting-state functional magnetic resonance imaging, NfL neurofilament light chain, GFAP glial fibrillary acidic protein.