Early Detection of GDM in First-Trimester Pregnant Women Using SVM Classifier
DOI:
https://doi.org/10.31294/jtk.v12i2.12783Keywords:
GDM, Pregnant Women, Classification, SVM, AUCAbstract
Indonesia is among the top five countries in the world with the highest number of people living with diabetes, including those in the productive age group and pregnant women. Gestational Diabetes Mellitus (GDM) is a common metabolic complication during pregnancy and is generally diagnosed at 24–28 weeks of gestation. Delayed detection of GDM can limit opportunities for early intervention and increase the risk of complications for both the mother and the fetus. Therefore, an early detection approach is necessary, particularly during the first trimester of pregnancy. The development of Artificial Intelligence (AI), especially Machine Learning (ML), provides opportunities to develop disease prediction models based on clinical data. One algorithm that has demonstrated strong performance in classification tasks is the Support Vector Machine (SVM). This study aims to evaluate the performance of an SVM model using the Confusion Matrix and Area Under the Curve (AUC) in the early detection of GDM among first-trimester pregnant women. The research methodology includes the collection and preprocessing of clinical data, implementation of the algorithm, and testing or evaluation of model performance. The results show an accuracy of 71% and an AUC value of 76%. This study is expected to contribute to the early detection of GDM in pregnant women, thereby helping to reduce the risk of diabetes in children after birth.
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