Identification and validation of an explainable machine learning model for early postoperative pulmonary complications after esophagectomy in patients with esophageal cancer

The CatBoost-based model exhibited robust predictive accuracy for early PPCs following esophagectomy, achieving an AUC of 0.888 in external validation. Integration of SHAP analysis enhanced interpretability, offering actionable insights for risk stratification and guiding perioperative clinical-making. This tool may facilitate timely interventions to mitigate postoperative morbidity in EC patients.