<![CDATA[Purpose – This study aims to develop and evaluate a machine learning-based preeclampsia risk prediction model integrated with payer-stratified health-economic analysis to assess the clinical feasibility, cost efficiency, and equity implications of digital obstetric screening in a multipayer healthcare system. Methods – A retrospective prediction-model and budget-impact study was conducted using de-identified electronic medical record and hospital billing data from 465 pregnant patients in an Indonesian tertiary referral hospital. Logistic Regression and XGBoost models were developed using routine clinical variables. Model performance was assessed through five-fold Group K-Fold cross-validation, calibration using Isotonic Regression, SHAP-based interpretability, Decision Curve Analysis, and payer-stratified economic evaluation across Private, Commercial Insurance, and BPJS groups. Findings – The XGBoost model achieved strong predictive performance with ROC-AUC of 0.9315 and PR-AUC of 0.8013. Calibration error approached 0.0000, indicating reliable probability estimates. SHAP analysis showed clinically plausible predictors, particularly systolic blood pressure, mean arterial pressure, and gestational age. A 1% threshold achieved 100% sensitivity, eliminating missed preeclampsia cases. Economically, model-guided screening reduced costs for Private and Insurance patients but increased costs for BPJS patients due to reimbursement misalignment. Research Implications – The findings indicate that clinical AI implementation requires not only predictive accuracy but also payer-aware financing strategies to prevent inequitable access. Originality – This study offers a novel integration of calibrated machine learning prediction, clinical interpretability, threshold optimization, and payer-stratified economic evaluation in preeclampsia screening.]]>
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