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Comprehensive Review Of Machine Learning Based Gestational Diabetes Mellitus: The Role Of SMOTE And ADSYN
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SUNDARI B,DR.T.NIRMALRAJ
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Keywords: Gestational Diabetes Mellitus, Class Imbalance, SMOTE, ADASYN, Synthetic Oversampling, Healthcare Data Balancing
Abstract
Gestational diabetes mellitus is a form of glucose intolerance that is first recognized during pregnancy and poses significant short and long term risks to both the mother and the fetus. Over the past several decades, diagnostic criteria for GDM have evolved in response to advancements in glucose testing and a growing body of evidence linking maternal hyperglycaemia to adverse perinatal outcomes. It happens when the hormones from the placenta block your ability to use or make insulin. Insulin helps your body maintain the right amount of glucose in your blood. Too much of glucose in your blood can lead to pregnancy complications. Gestational diabetes comes from hormonal changes and the way your body converts foods into energy. Early identification and intervention are essential to reduce adverse outcomes such as preeclampsia, cesarean delivery, macrosomia, and future development of type 2 diabetes mellitus. However, the prediction of GDM is often challenged by class imbalance, high- dimensional clinical data and the complexity of risk factors. The potential of hybrid machine learning approaches for reliable and early GDM prediction, supporting clinical decision making and personalized prenatal care. This review provides a comprehensive overview of class imbalance in GDM datasets and examines two widely adopted oversampling techniques, the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), for addressing this challenge. The review discusses the underlying concepts, mechanisms, advantages, and limitations of both techniques, highlighting their ability to generate synthetic minority-class samples and improve data distribution. It also compares the characteristics of SMOTE and ADASYN, emphasizing their applicability to healthcare datasets and their role in enhancing the representation of underrepresented clinical cases. Furthermore, the review summarizes recent research trends, identifies existing challenges associated with synthetic data generation, and outlines future directions for developing more robust and clinically relevant data balancing strategies. By providing a focused analysis of SMOTE and ADASYN in the context of GDM, this review aims to support future research on balanced healthcare datasets and contribute to more reliable and equitable predictive modelling in maternal healthcare.
IJCRT's Publication Details
Unique Identification Number - IJCRT2608459
Paper ID - 312637
Author type - Indian Author
Page Number(s) - e282-e296
Pubished in - Volume 14 | Issue 8 | August 2026
DOI (Digital Object Identifier) -
No Of Downloads - 63
Author Country - India, 631501, ADAMBAKKAM, CHENNAI 600 088, ADAMBAKKAM, CHENNAI 600 088, 631501, Science All
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SUNDARI B,DR.T.NIRMALRAJ, "Comprehensive Review Of Machine Learning Based Gestational Diabetes Mellitus: The Role Of SMOTE And ADSYN", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 8, pp.e282-e296, August 2026, Available at :http://www.ijcrt.org/papers/IJCRT2608459.pdf
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