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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