Risk Level Prediction of Antenatal Period Using Machine Learning Approaches

DOI

10.2991/978-94-6239-664-7_16How to use a DOI?

Keywords

Prediction Risk Level; Antenatal Period; ML; Gaussian Naive Bayes; Logistic Regression; Random Forest Classifier; Perceptron; EDA

Abstract

This research looks at the use of machine learning to predict the risk levels of antenatal complications, presenting a novel approach to improving proactive antenatal care. In this study, we have collected dataset of 800 entries with eight key attributes such as Age, Weight, BMI, systolic BP, diastolic BP, BloodglucoseLevels, BodyTemperature, and HeartRate. The target attribute, “Labels” categorizes pregnancies as “Low Risk,” “Medium Risk,” and “High Risk,” with possible level percentages (Low: 43.38%, Medium: 38.62%, High: 18%).A variety of machine learning models are used, including Gaussian Naive Bayes, Logistic Regression, Random Forest Classifier, Perceptron, and Voting Classifier. The results highlight the exceptional performance of the Random Forest Classifier, outperforming others with an accuracy of 99.21%. The ability of the model to collect multiple factors and minimize overfitting shows its potential to predict antenatal risk. The ethical considerations of the study ensure privacy for patients and avoid judgments, focusing on responsible implementation of models. The purpose of this study follows from its ability to change antenatal care by promoting an active structure that enhances healthier pregnancy and better outcomes for both the mother and the baby. The findings call for more research on understanding machine learning outputs, long-term effect evaluations, and the ability to adapt the proposed model in a number of healthcare settings. This study prepares the way for future studies that aim to improve maternal care through the use of advanced machine learning techniques.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Anika Nawar
AU  - Kazi Samiha
AU  - Shrabon Datta
AU  - Rakibul Hasan Akash
AU  - Narayan Ranjan Chakraborty
AU  - Tawhid Ahmed Komol
PY  - 2026
DA  - 2026/06/08
TI  - Risk Level Prediction of Antenatal Period Using Machine Learning Approaches
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 206
EP  - 220
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_16
DO  - 10.2991/978-94-6239-664-7_16
ID  - Nawar2026
ER  -