Abstract: Maternal and neonatal mortality rates pose a significant challenge in healthcare systems worldwide. Predicting the childbirth approach is essential for safeguarding the mother’s and child’s well-being. Currently, it is dependent on the judgment of the attending obstetrician. However, selecting the incorrect delivery method can cause serious health complications both in mother and child over short-time and long-time. This research harnesses machine learning algorithms’ capability to automate the delivery method prediction process. This research studied two different stackings implemented in machine learning, leveraging a dataset of 6157 electronic health records and a minimal feature set. Stack1 consisted of k-nearest neighbors, decision trees, random forest, and support vector machine methods, yielding an F1-score of 95.67%. Stack 2 consisted of Gradient Boosting, k-nearest neighbors, and CatBoost methods, which yielded 98.84%. This highlights the superior effectiveness of its integrated methodologies. This research enables obstetricians to ascertain the delivery method promptly and initiate essential measures to ensure the mother’s and baby’s safety and well-being.

Pusarla Sindhu and Parasana Sankara Rao. “Predicting the Most Suitable Delivery Method for Pregnant Women by Using the KGC Ensemble Algorithm in Machine Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.10 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01510106

@article{Sindhu2024,
title = {Predicting the Most Suitable Delivery Method for Pregnant Women by Using the KGC Ensemble Algorithm in Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01510106},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01510106},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Pusarla Sindhu and Parasana Sankara Rao}
}