Development and validation of an explainable machine learning model for predicting surgical intervention in pediatric intestinal obstruction.

Pediatric intestinal obstruction, a critical acute abdomen condition, carries a risk of intestinal necrosis. Decisions for urgent surgery lack standardized criteria. This study aimed to develop an explainable machine learning (ML) model to predict surgical indication, reducing diagnostic variability. A retrospective study included 642 children from our center (split into training/internal validation sets) and 137 from two external centers. Surgical indication was defined based on the presence of one or more of the following: evidence of peritonitis on physical examination, radiographic signs of strangulation, or failure of conservative management for > 24 h; in cases of diagnostic uncertainty, consensus among three senior physicians was used as the adjudication standard. We built models using 35 easily obtainable features across eleven algorithms and evaluated performance via AUC and other metrics. SHapley Additive exPlanations (SHAP) provided model interpretability, and an optimal probability threshold was established for clinical stratification. At the optimal threshold determined by maximizing the Youden index, the model achieved a sensitivity of 89% and a specificity of 99% in the internal validation set. The random forest model demonstrated the highest discriminative performance. A streamlined, interpretable model using the eight most important features (including vital sign dynamics and clinical markers) showed no evidence of overfitting based on the close agreement between training and validation performance metrics. It achieved AUCs of 0.988 (internal validation) and 0.981 (external validation). The model has been translated into a convenient clinical tool.

Conclusion

Our explainable ML model demonstrates strong discriminative performance in predicting the need for surgery in pediatric intestinal obstruction and is strongly associated with intestinal necrosis. By mitigating the "black-box" concern through SHAP, it offers transparent, interpretable decision support to clinicians.