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.