Interpretable Predictive Modeling of Postpartum Surgical Site Infections

Abstract

Background: Postpartum surgical site infections (SSIs) are a major cause of maternal morbidity in low-resource settings, yet early detection is limited by reactive, symptom-based surveillance systems. Ma chine learning (ML) and explainable artificial intelligence (XAI) offer potential tools for proac tive SSI risk identification using routinely collected clinical data. Methods: A retrospective dataset of 8,918 caesarean deliveries from three regional referral hospitals in Uganda (SSI prevalence: 8.1%) was analysed. After rigorous cleaning, leakage-free feature engineering, imputation, and encoding, four ML models Logistic Regression, Random Forest, Support Vector Machine (RBF), and XGBoost were trained and evaluated under extreme class imbalance using SMOTENC. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and precision–recall curves. SHAP explainability was applied to identify globally and locally influential predictors. Results: Overall discriminatory performance was modest due to limited predictive signal in routine clin ical records. Logistic Regression achieved the highest sensitivity (recall = 0.9931; ROC-AUC = 0.6528), whereas XGBoost produced the highest accuracy (0.7932) but poor recall (0.2639). SHAP analysis highlighted preoperative showering, skin antiseptic preparation, obstructed labour, and prolonged surgery duration as key contributors to increased SSI risk. Conclusion: Explainable ML models are feasible for early SSI risk identification in low-resource settings but are constrained by sparse routine data. Logistic Regression may serve as a high-sensitivity clinical screening tool. Improved performance will require enriched clinical datasets, inclu sion of intraoperative variables, and multi-centre validation to support practical deployment in maternal health surveillance.