Pregnancy mode Detection Predictive Model for Enhanced Maternal Healthcare

In the healthcare domain, Pregnancy of a woman is a complex physiological process during which numerous factors influence the mode of delivery, whether it would be a normal vaginal delivery (NVD) or a caesarean section (C-section). Understanding and predicting the mode of delivery are crucial for healthcare providers to ensure optimal maternal and fatal outcomes. Caesarean deliveries, while sometimes medically necessary, are associated with increased risks for both the mother and the baby compared to vaginal deliveries. Therefore, accurate prediction of the mode of delivery can aid healthcare providers in making informed decisions and implementing appropriate interventions to mitigate potential risks. This study focuses on Factors Affecting Mode of Delivery, Predicting Whether the Delivery will be Normal or C-Section Based on various related Factors. Machine learning (ML) techniques which offer a promising approach to analyse and predict pregnancy outcomes based on various maternal and prenatal factors are used to build a predictive model. The model will predict the mode of delivery as normal or caesarean using random forest, logistic regression and support vector machine classifiers. The accuracy of these models shows that performance of random forest is more accurate to predict mode of delivery based on the dataset.