Predicting Childbirth Complications in High-Risk Pregnant Women Using Machine Learning
DOI:
https://doi.org/10.59247/jahir.v4i1.412Keywords:
Machine Learning, Labor Complications, SHAP, Medical Prediction, Optimization of Midwifery CareAbstract
Childbirth complications remain one of the leading causes of high maternal mortality rates in Indonesia, primarily due to delayed detection at referral healthcare facilities, which hinders timely intervention. Most previous machine learning studies have focused on predicting a single outcome, even though high-risk pregnancies are prone to experiencing multiple complications simultaneously. This study aims to develop an explainable machine learning-based multi-class classification model utilizing Electronic Medical Record (EMR) data to support the early identification of six major childbirth complications: hypertension, preeclampsia, prolonged labor, premature rupture of membranes, hemorrhage, and infection. The study employed a retrospective cohort design involving 10,644 maternal records from the 2020–2024 period at Margono Soekarjo General Hospital. A total of 16 maternal, obstetric, clinical, and laboratory variables were analyzed using the Extreme Gradient Boosting (XGBoost) algorithm. The dataset was divided into training (80%) and testing (20%) sets, with class imbalance addressed through oversampling. Model evaluation was performed using accuracy, F1-score, and AUC, while interpretability was analyzed using Shapley Additive Explanations (SHAP). The model achieved an accuracy of 70%, a macro F1-score of 0.65, and a macro-AUC of 0.92, demonstrating good discriminatory ability. SHAP analysis identified metabolic, hemodynamic, and hematological factors as the primary predictors. These findings suggest the model’s potential as a tool for early risk stratification and clinical decision support, although external validation is still required
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