Enhancing Maternal Health: Logistic Regression for Predicting Postpartum Hemorrhage
DOI:
https://doi.org/10.53797/jthkkss.v7i1.6.2026Keywords:
Evolutionary Feature Selection, Logistic Regression, Postpartum Hemorrhage, Particle Swarm Optimization, SMOTEAbstract
Postpartum hemorrhage (PPH) is a leading cause of maternal mortality, especially in developing countries like Indonesia, where the Maternal Mortality Rate (MMR) remains high. Machine learning (ML) can enhance early PPH prediction, improving risk identification and clinical decision-making. Logistic Regression (LR) has shown strong performance in PPH prediction, with accuracy ranging from 69–92%. This study evaluates ML algorithms to improve PPH risk prediction and support timely clinical interventions. A retrospective cohort study analyzed 1,029 birth cases (326 PPH, 703 non-PPH) with 21 features covering maternal profiles, obstetric history, and health status. A logistic regression model was developed, utilizing SMOTE for class balancing and enhanced with Particle Swarm Optimization (PSO) and Evolutionary Feature Selection (EFS). EFS without SMOTE achieved the highest accuracy (86.78%) but had an imbalance with high sensitivity (93.74%) and low specificity (71.79%). PSO with SMOTE, while slightly less accurate (84.43%), had the highest AUC (0.905) and better balance between sensitivity (86.16%) and specificity (82.66%), effectively addressing class imbalance. Key PPH predictors include birth attendant, prolonged labor, and anemia level.
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Copyright (c) 2026 Dewi Pusparani Sinambela, Bahbibi Rahmatullah, Nita Hestiyana, Nurul Hidayah, Asmara Alias
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