Table 4 Classification with leave-one-out cross validation on the second wave dataset (validation).

From: Early outcome detection for COVID-19 patients

 

F1 score

Accuracy

  

Coverage 90%

Coverage 75%

 

Coverage 90%

Coverage 75%

Model

GA

DT-RFE

LR-RFE

DT-RFE

LR-RFE

GA

DT-RFE

LR-RFE

DT-RFE

LR-RFE

LR

0.764

0.787

0.744

0.773

0.728

0.778

0.807

0.775

0.795

0.772

DT

0.853

0.719

0.701

0.790

0.783

0.861

0.730

0.718

0.808

0.789

RF

0.811

0.725

0.700

0.811

0.744

0.819

0.750

0.711

0.821

0.764

NB

0.626

0.736

0.712

0.773

0.710

0.611

0.723

0.695

0.782

0.758

SVM

0.765

0.652

0.659

0.777

0.658

0.806

0.757

0.762

0.808

0.761

Support

72

296

298

78

360

72

296

298

78

360

  1. Each row corresponds to a different model type. We compare models trained on variables selected by our feature selection method (GA) with those selected by the two recursive feature elimination algorithms with the two different coverage thresholds (DT-RFE, LR-RFE). The bold font identifies the models with best performance, for each of the different rows (i.e. for each predictive model type).
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