Exploring Machine Learning Algorithms for Identifying Optimal Features to Predict Childbirth Modes

The process of childbirth is influenced by multiple factors, including maternal health, fetal conditions, and unforeseen medical complications. Accurately predicting the mode of delivery-whether vaginal birth or cesarean section-is crucial for healthcare professionals to make informed decisions regarding patient management. Machine learning techniques have shown significant potential in predicting various medical outcomes, including labor and delivery methods. In this study, we explore the application of Support Vector Machines (SVM) and Random Forest (RF) algorithms to develop predictive models for childbirth mode. SVM is a supervised learning technique that classifies data into distinct categories, while RF is an ensemble learning method that enhances prediction accuracy by combining multiple decision trees. The dataset used in this research consists of maternal demographics, medical history, and labor-related parameters. Our findings indicate that both SVM and RF models achieve high accuracy in predicting the mode of delivery, highlighting their effectiveness in assisting healthcare providers with early decision-making and improving maternal and neonatal care.