Background: Acute appendicitis is the most common abdominal surgical emergency, with diagnostic uncertainty greatest in resource-limited settings. Objectives: To develop and internally validate an interpretable, laboratory-driven machine learning approach to assist clinical decision-making in suspected appendicitis, including diagnosis, perforation detection, and surgical severity stratification. Methods: A retrospective cohort of 246 patients with histopathologically confirmed appendicitis and 45 controls with similar abdominal pain was analyzed at a secondary-level hospital in Mexico. After cleaning and imputation, 41 laboratory variables were used to train three models: Random Forest for appendicitis detection and perforation identification, and Support Vector Machine for surgical severity stratification. Class imbalance was addressed with synthetic oversampling, and feature selection prioritized clinical interpretability. Results: Appendicitis detection achieved excellent discrimination (AUC = 0.94), correctly identifying 90% of cases, with 100% specificity. The perforation model reached 100% sensitivity (AUC = 0.875), prioritizing safe detection of high-risk cases, while severity stratification showed moderate performance (AUC = 0.721), correctly identifying 81% of complicated cases without imaging. Conclusions: Laboratory-based ML models accurately detected acute appendicitis and identified all perforated cases using routine data alone, while surgical severity stratification showed moderate discrimination in the absence of imaging. These findings demonstrate the feasibility of laboratory-driven decision support for early risk assessment in resource-limited emergency settings and support further external validation.
Martinez-Fierro et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: