Ischemic heart disease (IHD), stroke, lower extremity peripheral arterial disease (LEPAD), diabetes mellitus, and chronic kidney disease (CKD) have become the main causes of death and disability globally [1,2]. According to research conducted by Jie Li and colleagues in 2021, IHD caused approximately 9.0 million deaths. Stroke accounted for ~ 7.3 million deaths, diabetes mellitus led to […]

Differentiating acute exacerbation of chronic obstructive pulmonary disease (AECOPD) from acute heart failure (AHF) is clinically challenging due to overlapping symptoms, especially in resource-limited settings lacking radiological/ultrasonographic tools. This study developed an eXtreme Gradient Boosting (XGBoost) model for differential diagnosis using Database: Medical Information Mart for Intensive Care (MIMIC) and two Chinese hospital cohorts, comparing it with a guideline-based model and applying Shapley Additive Explanations (SHAP) analysis to identify key biomarkers. The XGBoost model showed high discriminatory performance (area under the curve [AUC]: 0.94–0.98 across development/validation, outperforming the guideline-based model’s AUC of 0.53) with consistent accuracy across age/sex subgroups. Key biomarkers included NT-proBNP and total bilirubin. This robust model enables rapid, accurate differential diagnosis in resource-constrained emergency settings.