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Table 4 The performance of ML algorithms before and after preprocessing

From: Predictive modeling for COVID-19 readmission risk using machine learning algorithms

ML algorithm

Accuracy

Precision

Recall

Specificity

F-Measure

F.a. r

p-value

b.p

a.p

b.p

a.p

b.p

a.p

b.p

a.p

b.p

a.p

Decision tree

0.761

0.958

0.564

0.961

0.534

0.903

0.906

0.982

0.547

0.922

2.04

0.0091

SVM

0.457

0.821

0.287

0.743

0.412

0.792

0.375

0.921

0.336

0.767

4

0.0001

KNN

0.526

0.941

0.462

0.942

0.485

0.765

0.912

0.961

0.471

0.823

2.201

0.0063

Proposed model

0.782

0.9705

0.8064

0.9729

0.8333

0.9863

0.7

0.9259

0.8196

0.9795

2.187

0.0065

  1. b.p Before preprocessing, a.p after preprocessing, F.a. r Friedman aligned ranks