Machine Learning for Predicting NTSV Cesarean Risk: Transforming Obstetric Care through Data-Driven Insights

The precise forecasting of Nulliparous, Term, Singleton, Vertex (NTSV) cesarean deliveries has become crucial for minimizing superfluous procedures and enhancing maternal and neonatal outcomes. This research employs machine learning approaches to evaluate cesarean delivery risk in NTSV patients within hospital environments, satisfying the requirements of proactive obstetric risk management. We assessed several machine learning techniques, including random forests, support vector machines, logistic regression, and neural networks, using a dataset of 1,065 patient records to predict cesarean compared to vaginal deliveries. The random forest classifier demonstrated the best performance amongst the different models, with an accuracy of 67.38%, an AUC of 0.6082, along with an F1 score of 0.6241, emphasizing maternal age, BMI, and systolic blood pressure as key factors in assessing cesarean risk.

Our research demonstrates that using machine learning models with sophisticated visualization tools may substantially improve the prediction and control of NTSV cesarean deliveries, enhancing both maternal and neonatal outcomes. This technique illustrates the capability of incorporating predictive analytics into clinical processes to enhance evidence-based practices in obstetric care, facilitating wider applicability across other healthcare environments. The current approach exhibits intriguing outcomes; nevertheless, additional research will concentrate on improving accuracy through extensive feature engineering and hyperparameter optimization techniques.