DRLPARTO: A Machine Learning Based Partograph for Fetal Monitoring System

Deepa J., Nandhyala Geetha Reddy, P. Sai Kiran, P. V. Sai Ram Reddy

2025

Abstract

The review focuses on predicting the mode of delivery using machine learning techniques. Our approach involves developing a machine learning model that evaluates partograph data to anticipate possible complications or the necessity for medical interventions. Although various perspectives exist on the application of partographs, a comprehensive understanding of their implementation remains unclear. The proposed model assesses multiple parameters, considering the mother’s health status, fetal condition, and the ongoing labor progression. The primary goal is to identify the most effective algorithms for predicting delivery outcomes, specifically distinguishing the types of delivery i.e, normal or cesarean deliveries using machine learning techniques. Supervised learning algorithms such as Decision Trees, Random Forest, and Logistic Regression were employed with the proposed method DRLPARTO achieving 93% accuracy and consistently high precision, recall, and F1 score (92-93%), demonstrating its robustness and effectiveness in the given task.

Paper Citation

in Harvard Style

J. D., Reddy N., Kiran P. and Reddy P. (2025). DRLPARTO: A Machine Learning Based Partograph for Fetal Monitoring System. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 553-558. DOI: 10.5220/0013932800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Deepa J. and Nandhyala Reddy and P. Kiran and P. Reddy},
title={DRLPARTO: A Machine Learning Based Partograph for Fetal Monitoring System},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={553-558},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013932800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - DRLPARTO: A Machine Learning Based Partograph for Fetal Monitoring System
SN - 978-989-758-777-1
AU - J. D.
AU - Reddy N.
AU - Kiran P.
AU - Reddy P.
PY - 2025
SP - 553
EP - 558
DO - 10.5220/0013932800004919
PB - SciTePress