From: Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence
AI technique | Original (row, imbalanced) data | Post-processed (rebalanced) cohorts | ||||
---|---|---|---|---|---|---|
Model | Model | |||||
LASSO linear regularized model | Performance of testing data | Model performance of testing data | ||||
 | Confusion matrix | Result false | Result true | Confusion matrix | Result false | Result true |
 | Prediction false | 4759 | 172 | Prediction False | 4202 | 38 |
 | Prediction true | 14 | 68 | Prediction True | 571 | 202 |
 | Accuracy | 96.29% |  | Accuracy | 87.85% |  |
 | 95% CI | 95.73% | 96.80% | 95% CI | 86.92% | 89.74% |
 | Kappa | 0.4079 |  | Kappa | 0.3514 |  |
 | Sensitivity | 0.28333 |  | Sensitivity | 0.84167 |  |
 | Specificity | 0.99707 |  | Specificity | 0.88037 |  |
 | Prevalence | 0.01636 |  | Prevalence | 0.15420 |  |
Random forest | ||||||
---|---|---|---|---|---|---|
Confusion matrix | Result false | Result true | Confusion matrix | Result false | Result true | |
Neural networks | Prediction false | 6120 | 236 | Prediction false | 6058 | 225 |
 | Prediction true | 99 | 110 | Prediction true | 161 | 121 |
 | Accuracy | 94.897% |  | Accuracy | 94.120% |  |
 | Precision | 0.5263 |  | Precision | 0.4291 |  |
 | AUC | 0.7252 |  | AUC | 0.7134 |  |
 | Recall | 0.3179 |  | Recall | 0.3497 |  |