Short-term mortality prediction in children with gastrointestinal congenital anomalies using a random forest classifier

To our knowledge, this is the first global study to apply machine learning for mortality prediction in children with gastrointestinal malformations, using data from 3849 patients across 74 countries. The random forest model achieves 88.84% (CI: 86.79%, 90.66%) accuracy, with strong precision (84.13%, CI: 78.4%, 88.8%) and sensitivity (89.98%, CI: 87.8%, 91.9%) in identifying at-risk patients. Key predictors include clinical factors (diagnosis of complications, American Society of Anaesthesiologists score, weight on admission, duration of post-operative antibiotic treatment), procedural elements (surgical checklist), and socio-demographic variables (continent, income level).