Feng et. al. constructed and validated a machine learning-based model for identifying the risk of fear of childbirth (FOC) in pregnant women during late pregnancy. Predicting the probability of FOC in expectant mothers is valuable to clinicians for improving childbirth experiences and ensuring patient-centered care. A cross-sectional observational study with 406 pregnant women was performed and FOC was evaluated based on fear related to the baby’s health, fear of losing control during childbirth, fear of pain and injury, and fear of interventions in the hospital. An FOC prediction model was developed using 6 machine learning algorithms: Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGM), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN). The XGB model achieved the highest performance (AUC=0.907), followed by the LR model (AUC=0.893). The LR model helped create a nomogram using the predictors of pain catastrophizing, preference for painless delivery, desired mode of delivery, medication use during early pregnancy, and use of pregnancy-related apps. The prevalence of FOC among pregnant women was found to be 72.4%, demonstrating the necessity for early prediction and intervention for FOC.