Machine learning‐based prediction of postpartum hemorrhage using maternal clinical and biochemical features

OBJECTIVE: This study evaluates the predictive performance of various machine learning (ML) algorithms for postpartum hemorrhage (PPH), peripartum hysterectomy, and severe coagulopathy using routinely collected pre-delivery clinical and biochemical data. METHODS: In this retrospective cohort study, data from 566 deliveries at a tertiary obstetric center between 2019 and 2025 were analyzed. A total of 283 patients with PPH and 283 matched controls were included. Twenty maternal variables, including hemoglobin, body mass index, uterine scar, and comorbidities, were used to develop ML models: support vector machine (SVM), logistic regression, random forest, gradient boosting, and naive Bayes. Model performance was evaluated using accuracy, F1 score, and area under the ROC curve (AUC). Reduced-feature models with ten predictors were also assessed. RESULTS: The SVM model demonstrated the highest performance for PPH prediction (accuracy: 83.3%, AUC: 0.903), followed closely by logistic regression (AUC: 0.902). Reduced-feature models maintained high performance (AUCs >0.88), indicating feasibility for practical deployment. Random forest achieved the best performance for predicting hysterectomy (AUC: 0.88) and coagulopathy (AUC: 0.90). Key predictors included low pre-delivery hemoglobin, prolonged active labor phase, uterine scar, and preterm delivery. CONCLUSION: Machine learning models can reliably identify patients at risk for postpartum hemorrhage and its complications using accessible pre-delivery data. The robustness of reduced-variable models enhances their clinical utility, especially in resource-limited settings. Integration of such algorithms into electronic health record systems might support early intervention and improved maternal outcomes.