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Table 5 Model performance indices of the LR, DT, BF algorithms for Model I, II, and III in training data

From: Association between biochemical and hematologic factors with COVID-19 using data mining methods

Model I

(a) LR

(b) DT

Actual

Predicted Count

Actual

Predicted Count

COVID Positive

No

Yes

COVID Positive

No

Yes

No

3328

675

No

5149

758

Yes

1075

1959

Yes

2061

2568

Sensitivity = 83.14%

AUC = 80.74%

Sensitivity = 87.17%

AUC = 80.23%

Precision = 75.58%

Accuracy = 75.13%

Precision = 71.41%

Accuracy = 73.24%

(c) BF

 

Actual

Predicted Count

COVID Positive

No

Yes

No

5718

189

Yes

819

3810

Sensitivity = 96.80 %

AUC = 98.06 %

  

Precision = 87.47 %

Accuracy = 90.43 %

  

Model II

(d) LR

(e) DT

Actual

Predicted Count

Actual

Predicted Count

COVID Positive

No

Yes

COVID Positive

No

Yes

No

4074

1175

No

4506

1401

Yes

1764

1175

Yes

1401

2925

Sensitivity = 77.61 %

AUC = 77.37 %

Sensitivity = 76.28 %

Sensitivity = 76.28 %

Precision = 69.78 %

Accuracy = 68.28 %

Sensitivity = 76.28 %

Sensitivity = 76.28 %

(f) BF

 

Actual

Predicted Count

COVID Positive

No

Yes

No

5488

419

Yes

1262

3367

Sensitivity = 92.91 %

Precision = 81.30 %

  

Precision = 81.30 %

Accuracy = 84.05 %

  

Model III

(g) LR

(h)DT

Actual

Predicted Count

Actual

Predicted Count

COVID Positive

No

Yes

Predicted Count

No

No

No

3890

871

No

5282

625

Yes

1273

2427

Yes

2176

2453

Sensitivity = 66.08%

AUC = 80.37 %

Sensitivity = 66.00%

Precision = 72.88%

Precision = 74.93%

AUC = 80.37 %

Precision = 72.88%

Precision = 72.88%

(i) BF

 

Actual

Predicted Count

COVID Positive

No

Yes

No

5808

99

Yes

647

3982

Sensitivity = 66.08%

AUC = 99.00 %

  

Precision = 74.93%

Accuracy = 69.63%

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