EARLY PREDICTION OF CAESAREAN SECTION USING AN ENHANCED MACHINE LEARNING MODELS

Abstract

The early prediction of the need for a Caesarean Section (C-section) is crucial in ensuring maternal and neonatal safety by enabling timely medical intervention and effective birth planning. This study presents an Enhanced Machine Learning (ML) approach for predicting the likelihood of a C-section based on key maternal, fetal, and clinical parameters such as maternal age, blood pressure, fetal heart rate, gestational period, and medical history. The proposed model integrates advanced pre processing, feature selection, and ensemble learning techniques to enhance prediction accuracy and reliability. Various algorithms—including Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Artificial Neural Networks (ANN)—are compared to determine the most optimal predictive framework. The enhanced ML model demonstrated superior performance in terms of accuracy, precision, recall, and F1-score, outperforming traditional statistical methods. The results highlight the potential of the proposed system as a decision-support tool for obstetricians to anticipate delivery complications and recommend suitable delivery modes, thereby improving maternal healthcare outcomes. Cesarean section is suggested in circumstances in which vaginal birth presents a more prominent probability of unfavourable maternal or prenatal results than ordinary. Be that as it may, it is related with a higher danger of inconveniences, particularly when performed without an unmistakable clinical sign. Since work chaperons have no normalized clinical technique to aid this choice, measurable devices created dependent on numerous work factors might be another option. For hopeful guardians, a first birth is prominent for its unconventionality, and the way to safe work and conveyance is generally convoluted by a necessity for impromptu cesarean conveyance. The capacity to envision a straightforward vaginal birth, or to foresee the necessity for impromptu cesarean conveyance, conveys the possibility to encourage ideal birth decisions.