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Table 3 Predictor variables in ASCVD models

From: Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population

Rank

ANN-based ASCVD prediction model

One minus AUC after permutations

1

Age

0.6458121

2

SBP

0.7290980

3

V2.R Area

0.7464859

4

V2.Max R Amplitude

0.7522701

5

I.T Area (Full)

0.7565553

6

V2.S Area

0.7570991

7

V4.Max S Amplitude

0.7575463

8

V3.QRS Area

0.7575922

9

CR

0.7578144

10

I.T Duration

0.7582132

11

V6.T Area (Full)

0.7585335

12

V6.T Area

0.7590182

13

eGFR

0.7591497

14

I.T Peak Amplitude

0.7594988

15

GLU

0.7600205

16

V2.Max S Amplitude

0.7602828

17

V3.Max S Amplitude

0.7606117

18

V2.QRS Area

0.7607344

19

V6.Max R Amplitude

0.7615041

20

Peak E Wave Velocity

0.7618305

21

WBC

0.7618512

22

UA

0.7618903

23

I.P Area (Full)

0.7619013

24

aVR.T Area

0.7619031

25

DBP

0.7619409

26

V1.QRS Area

0.7619643

27

V3.S Area

0.7620318

28

I.T Area

0.7622241

29

V2.T Duration

0.7623373

30

V6.QRS Area

0.7628234

  1. The importance of each feature was quantified using the permutation feature importance method which measures the importance of a feature by calculating the decrease in the model’s performance (area under the ROC curve) after permuting its values. The higher their values, the more important the feature is. Features are sorted according to permutation importance
  2. Abbreviations: ANN Artificial Neural Network, SBP Systolic Blood Pressure, CR creatinine, eGFR Estimated Glomerular Filtration Rate, GLU glucose, WBC White Blood Cell, UA Uric Acid, DBP Diastolic Blood Pressure
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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