ISSN : 2488-8648
International Journal of Basic Science and Technology
A publication of the Faculty of Science, Federal University Otuoke, Bayelsa State
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ISSN : 2488-8648
Home About IJBST For Authors Issues Useful Downloads Contact
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×published date:2025-Apr-02
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Abstract
Gestational diabetes Mellitus, often referred to as pregnancy-related diabetes, is a type of diabetes that occurs during pregnancy. It can develop at any stage and is characterized by high blood sugar levels. If left undetected and untreated, gestational diabetes mellitus can result in complications for both the mother and the unborn child before, during, and after birth. The primary objective of this study is to develop and evaluate predictive models that can identify pregnant women at risk for gestational diabetes, this will help identify high-risk mothers who require earlier treatment, monitoring, and medication. The clinical decision support system proposed in this study for diagnosing Gestational Diabetes Mellitus was developed using the electronic data of routine antenatal care of pregnant women obtained from the UCI machine learning data repository. We employed various machine learning algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest Classifier, and Gradient Boosting Classifier, to develop and evaluate classification models aimed at early detection of gestational diabetes in pregnant women. The models were trained using 70% of the data and validated with the remaining 30%. To assess the performance of these predictive models, we compared them based on several evaluation metrics, including accuracy, recall, precision, and the AUC score. In the validation dataset, the Support Vector Machine (SVM) model outperformed other classifiers, achieving an accuracy of 98%, a recall of 95%, a precision of 100%, and an AUC score of 100%. An exploratory analysis of the Gestational Diabetes Mellitus (GDM) dataset identified several factors associated with an increased risk of developing Gestational Diabetes Mellitus, including age, the number of pregnancies, diastolic blood pressure, gestation in previous pregnancies, and family history. The results from the Support Vector Machine model demonstrated high accuracy, interpretability, and superiority in predicting Gestational Diabetes Mellitus using the GDM dataset.
Keywords: Clinical Decision Support System, Artificial Intelligence,,,
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Copyright © International Journal of Basic Science and Technology | Faculty of Science, Federal University Otuoke 2019. All Rights Reserved.
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