Explainable Machine Learning for Failed Labor Induction Prediction Using Nationwide Insurance Data

Failed labor induction presents a significant challenge, posing considerable risks for maternal health. The development of accurate predictive models is essential for assisting healthcare providers in determining the most suitable delivery methods. This study explores the application of machine learning (ML) models to predict failed labor induction, utilizing data from the Indonesian National Health Insurance (INHI). A retrospective cohort of 9-month period of pregnancy was established using INHI Sample Data, comprising 27,953 pregnancy cases. Failed labor induction was identified through ICD-10 codes (O61). The features considered included demographic data (age, insurance class, region) and binary-encoded diagnoses. Various ML models were assessed for their predictive performance. The ensemble model demonstrated a high area under the curve (AUC) of 0.759 (95% CI, with a balanced trade-off between sensitivity (0.816) and specificity (0.589). SHAP analysis was used to interpret feature contributions, highlighting both clinical and demographic factors influencing model predictions. ML models showed strong potential in predicting failed labor induction using administrative data. While the ensemble model achieved the best overall performance, simpler models such as logistic regression or XGBoost may offer greater practicality for clinical integration in Indonesia.