Title
Machine learning based prediction of gestational diabetes mellitus using early pregnancy biomarkers and clinical data
Authors
Karnaditya Rana1,*, Bikramaditya Mukherjee2 & Ajith Antony3
Affiliation
1Department of Clinical Data Management, Data Services, Tempus AI, Texas, USA; 2Department of Biochemistry, KPC Medical College, Jadavpur, Kolkata, India; 3Department of Forensic Medicine & Toxicology, P.K. Das Institute of Medical Sciences, Vaniyamkulam, Palakkad, Kerala, India; *Corresponding author
Karnaditya Rana - E-mail: dr.karan.rana@gmail.com
Bikramaditya Mukherjee - E-mail: bikramaditya217@gmail.com
Ajith Antony - E-mail: dr.ajithantony@gmail.com
Article Type
Research Article
Date
Received May 1, 2026; Revised May 31, 2026; Accepted May 31, 2026, Published May 31, 2026
Abstract
Gestational diabetes mellitus (GDM) is a common pregnancy-related condition that can lead to significant maternal and neonatal complications, but conventional screening methods often delay diagnosis. Therefore, it is of interest to develop a machine learning model for early prediction of GDM using first-trimester biomarkers and clinical data. Hence, a prospective study of 100 pregnant women was conducted and various machine learning algorithms were trained to predict GDM. The random forest model showed the best performance with an accuracy of 86% and an AUC of 0.90. Thus, we show the potential of machine learning in enabling early prediction and timely intervention for GDM, improving maternal and neonatal outcomes.
Keywords
Gestational diabetes mellitus (GDM), machine learning (ML), early pregnancy biomarkers, insulin resistance, predictive modeling
Citation
Rana et al. Bioinformation 22(5): 2838-2842 (2026)
Edited by
Ritik Kashwani
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
