APPLYING MACHINE LEARNING TO PREDICT GESTATIONAL DIABETES

  • Pham Minh Chuan Hung Yen University of Technology and Education
  • Phan Quang Huy National Hospital of Obstetrics and Gynecology
Keywords: Gestational diabetes, Machine Learning, Decision Tree, Support Vector Machine, Random Forest, Gradient Descent

Abstract

Gestational diabetes is a type of diabetes diagnosed for the first time during pregnancy that can affect the health of pregnancy and fetus. Patients who have had gestational diabetes in the past have an increased risk of developing type 2 diabetes. Therefore, detecting and predicting the likelihood of gestational diabetes by machine learning is essential. This paper applies machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest and Gradient Descent to predict pregnant women (gestational age from 24 to 28 weeks) whether you have gestational diabetes. Experimental results show that the accuracy of prediction is quite high, approximately from 93 % to more than 96 %. This will make it easier for clinicians to manage gestational diabetes, especially during the last trimester of pregnancy, when blood sugar levels are often high.

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Published
2022-03-31
How to Cite
Pham Minh Chuan, & Phan Quang Huy. (2022). APPLYING MACHINE LEARNING TO PREDICT GESTATIONAL DIABETES. UTEHY Journal of Science and Technology, 33(1), 34-40. Retrieved from https://tapchi.utehy.edu.vn/index.php/jst/article/view/519