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Table 2 Confusion matrix for predicting presence of PASC among females and males in the test dataset using the stochastic gradient boosting model created from the training dataset

From: Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care

 

Observed

Predicted

No PASC

PASC

Total

Females (n = 9203)

 No PASC

5863

347

6210

 PASC

1806

1187

2993

 Total

7669

1534

9203

Males (n = 5067)

 No PASC

3505

173

3678

 PASC

711

678

1398

 Total

4216

851

5067

  1. In females, predictions were based on 15,377 trees, with sensitivity 0.774 and specificity 0.765. In males, predictions were based on 11,221 trees, with sensitivity 0.797 and specificity 0.831
  2. PASC, post-acute sequelae of COVID- 19
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BMC Medicine

ISSN: 1741-7015

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