Machine learning-based prediction of maternal continuum of care completion: Evidence from Bangladesh Demographic and Health Survey 2022

Despite decades of investments in maternal healthcare, the completion of the maternal continuum of care (CoC), defined as receiving at least four antenatal care visits with a medically trained provider, delivery by a skilled birth attendant at a health facility, and postnatal care within 48 hours, remains low in Bangladesh. This study aimed to develop a machine learning-based prediction model to identify women most likely to complete the maternal CoC, using data from the 2022 Bangladesh Demographic and Health Survey. The analysis included 5,128 ever-married women aged 15–49 who had a live birth in the five years preceding the survey. After data pre-processing, including handling class imbalance through Synthetic Minority Oversampling Technique (SMOTE), 16 predictor variables were selected using the Boruta feature selection algorithm. Seven supervised machine learning algorithms were developed and evaluated, including Random Forest, XGBoost, support vector machines, logistic regression, and artificial neural networks. Among the 5,128 women, only 25.3% completed the maternal CoC in Bangladesh. The random forest performed best with an accuracy of 82%, precision of 81%, recall of 71%, and an AUC of 0.889. Feature importance and SHAP analysis identified wealth index, husband’s and respondent’s education, media exposure, and women’s decision-making autonomy as the most influential predictors. Identifying women at high risk of dropout can enable healthcare providers to deliver targeted counseling and interventions. These findings offer valuable insights to inform data-driven policies and strategies to enhance maternal health service utilization and reduce preventable maternal morbidity and mortality in low-resource settings like Bangladesh. • Only 25% of Bangladeshi women completed the maternal continuum of care. • Random Forest achieved the highest performance (AUC = 0.889, accuracy = 82%). • Greater wealth status, higher educational attainment, exposure to mass media, and higher women’s autonomy in major household decision-making were the strongest predictors of CoC completion. • ML models can help target high-risk women for counseling and improve maternal health uptake.