Predictive Analytics for Childbirth Mode Classification Using Machine Learning Techniques

Abstract: The birthing process is crucial to the mother's health and the relationship she forms with her infant. For the sake of both mother and child, the choice about the mode of delivery must be swiftly implemented. It is challenging for healthcare providers to make prompt and accurate choices in this area since mistakes in this area may have major effects on the mother's and fetus's health. A machine learning-based decision-support system for determining the most secure delivery mode is introduced in this study. K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree, Support Vector Machine (SVM), and a stochastic classifier are among the supervised learning techniques that are evaluated. This paper's results show that the Random Forest algorithm is more accurate at predicting the delivery mode, which helps doctors make better decisions and keeps mums and babies safer.

Keywords: childbirth prediction, machine learning, decision support system, delivery mode classification, maternal and infant health