Leveraging Real-Time Data for Stillbirth Prevention and Risk Mitigation

This study leverages real-time data and predictive analytics to mitigate stillbirth risks using machine learning techniques. We utilized the Cardiotocography (CTG) dataset to classify fetal health states as Normal, Suspect, or Pathologic. The preprocessing included data cleaning, correlation-based feature selection, and scaling. Eight machine learning models—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and LightGBM—were trained and evaluated using standard metrics. Bayesian hyperparameter tuning was employed for performance enhancement. SHAP was used to explain feature importance. Ensemble models like LightGBM and XGBoost achieved the highest performance (F1-score ~0.992).