TABLE 3.
Impact of variables on the AUC generated by machine learning models
| Model | AUC | ||
|---|---|---|---|
| All variables | 0.589 | ||
| All but Mayo partial | 0.590 | ||
| All but last FC | 0.460 | ||
| AUC | Precision | Recall | |
| Final model | 0.754 | 1 | 0.25 |
| Final model with data augmentation | |||
|---|---|---|---|
| AUC | SD | ||
| SMOTE | 0.747 | 0.025 | |
| ADASYN | 0.756 | 0.013 | |
Note: The final model contains FC, age, length of disease, firstFC/lastFC, number of drugs, Mayo partial scores, disease location, past disease severity. Precision minimizes false positives, while recall minimizes false negatives. The final model was tested with augmented datasets by different methods. Low SD shows the stability and the consistency of selected model.
Abbreviations: ADASYN, Adaptive Synthetic; AUC, area under the curve; FC, fecal calprotectin; SD, Standard Deviation; SMOTE, Synthetic Minority Oversampling TEchnique.