Machine Learning Approaches Predicting the Best Features of Child Birth Using Support Vector Machine (SVM) Algorithm

Abstract

Cesarean childbirth, though often necessary for medical reasons, carries inherent risks and impacts maternal and neonatal health. Predicting the likelihood of cesarean delivery can assist healthcare providers in making informed decisions and optimizing maternal care. In this study, we propose an ensemble machine learning approach to predict cesarean childbirth based on maternal and fetal health data. First, we collect comprehensive data including maternal age, BMI, gestational age, fetal weight, previous cesarean history, and other relevant factors. After preprocessing the data to handle missing values and encode categorical variables, we select informative features using correlation analysis and domain expertise. Next, we employ various ensemble machine learning methods, including Voting Classifiers, Bagging, Random Forest, Boosting, and Stacking. These methods combine predictions from multiple base learners, leveraging their diverse strengths to enhance predictive accuracy.