Early Prediction of Gestational Diabetes Using Machine Learning Models with Non Invasive Clinical Features

DOI

10.2991/978-94-6239-664-7_22How to use a DOI?

Keywords

Gestational Diabetes Mellitus; Early Detection; Machine Learning; Predictive Modeling

Abstract

Gestational diabetes mellitus is linked to adverse maternal and neonatal outcomes, which can be improved through early prediction. This current study evaluates the predictive performance of several machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting Tree, and Extreme Gradient Boosting by using a publicly available dataset consisting of 1013 records. The dataset included six predictive features: age, pregnancy number, weight, height, body mass index, heredity, and the outcome variable indicate the presence or absence of gestational diabetes. Data pre-processing steps included outlier detection, standardization, and Synthetic Minority Oversampling Technique for class balancing. Model performance was evaluated using accuracy, precision, recall, specificity, and F1 score. LightGBM achieved the highest overall accuracy (89.62%), followed by XGBTree (88.46%) and RF (87.69%). Our findings align with prior research showing the superiority of ensemble models in capturing complex feature interactions.

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.

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TY  - CONF
AU  - Khadiza Akter
AU  - Most. Jannatul Ferdows
AU  - Mohammed Motaher Hossain
PY  - 2026
DA  - 2026/06/08
TI  - Early Prediction of Gestational Diabetes Using Machine Learning Models with Non Invasive Clinical Features
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 314
EP  - 327
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_22
DO  - 10.2991/978-94-6239-664-7_22
ID  - Akter2026
ER  -