Abstract

Objectives: This study aims to develop a clinically applicable and interpretable decision tree-based model using routine clinical and laboratory parameters to differentiate acute appendicitis (AA) from nonspecific abdominal pain (NAP) in pediatric patients.

Patients and methods: This retrospective study included 708 pediatric patients presenting with abdominal pain between January 2019 and December 2023. Demographic characteristics, physical examination findings, laboratory parameters, and Alvarado scores (AS) were analyzed. The dataset was divided into training and held-out internal validation sets using stratified sampling to preserve class distribution. Model performance was evaluated using stratified five-fold cross-validation on the training set, while the held-out internal validation set was kept completely unseen during model development. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique applied exclusively within training folds. The dataset was partitioned using stratified sampling, and a decision tree model based on the classification and regression tree algorithm with an entropy splitting criterion was constructed. Model performance was evaluated on the heldout internal validation set using area under the curve (AUC), precision-recall (PR)-AUC, and F1-score.

Results: A total of 708 patients categorized into two groups: NAP group (296 males, 307 females; mean age: 10.20 ± 3.81 years, range, 4 to 17 years) and AA group (68 males, 37 females; mean age: 11.21 ± 3.51 years, range, 5 to 17 years) presenting with abdominal pain were recruited. Patients with AA had significantly higher white blood cell counts, neutrophil percentages, neutrophil-tolymphocyte ratios, C-reactive protein levels, and AS than those in the NAP group (all p < 0.001). Right lower quadrant (RLQ) tenderness emerged as the most important discriminative variable, followed by the AS, left shift, age, and anorexia. The decision tree model achieved an F1-score of 0.884, an AUC of 0.946, and a PR-AUC of 0.8545. Importantly, AA was associated with a very low likelihood in patients without RLQ tenderness and with low AS, defining a clinically meaningful low-risk subgroup.

Conclusion: Decision tree-based models may serve as valuable adjuncts to clinical judgment in evaluating suspected pediatric AA, particularly by identifying low-risk patients for whom further diagnostic testing or imaging is unlikely to alter clinical management.

Citation: Kurtuluş Ş, Battal F, Yücebaş SC, Köseoğlu M. Differentiating acute appendicitis in children in the emergency department: A decision tree-based model. Turkish J Ped Surg 2026;40(2):53-64. doi: 10.62114/JTAPS.2026.248.