• Soumya Madduru
• N.Sasi Priya
• V.Namratha
• S.Sonika
• J.Venu

Nowadays, the method of delivery is a crucial factor in ensuring the well-being of both mother and baby. Currently, the decision on the mode of delivery is usually made by the attending physician, but if the wrong method is chosen, it can lead to various short-term and long-term health problems for both the mother and baby. The number of cases where doctors unnecessarily suggest a cesarean delivery is on the rise, and human error can also play a role in choosing the incorrect mode of delivery. To mitigate these risks, We Proposed a machine learning-based decision-making model to predict the most appropriate mode of childbirth. To attain our objective five different machine learning algorithms XGBoost, KNN, Random Forest, SVM, and Logistic regression models has developed based on the 22 features which include age, amniotic fluid, height, and weight. Among all the algorithms XGBoost got the highest accuracy with 83%.

Machine Learning, mode of Delivery, XGBoost, KNN, Random Forest, SVM, Logistic Regression, Amniotic fluid

"PREDICTING THE MODE OF CHILDBIRTH USING MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.f493-f500, April-2023, Available :http://www.jetir.org/papers/JETIR2304564.pdf

"PREDICTING THE MODE OF CHILDBIRTH USING MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppf493-f500, April-2023, Available at : http://www.jetir.org/papers/JETIR2304564.pdf