Enuresis, Miscelleneous
| Article DOI | https://doi.org/10.1055/s-0040-1715655 |
| Objective | To predict enuresis in children |
| AI Approach | Logistic regression, Trees, Bayes, SVM, Deep Learning |
| Data Source(s) | Administrative dataset (8071 children) |
| Model Input | 14 variables (clinical factors, urinary habits, family history, lower urinary tract symptoms) |
| Model Outcome | Enuresis |
| Model Metrics | AUROC = 0.81, accuracy = 81% |
| Model Usability | NA |
AIPEDURO 
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