| Machine Learning-Based Paradigm for Diagnosis of Gestational Diabetes |
| Paper ID : 1017-ICEEM2023 (R1) |
| Authors: |
|
Neven Saleh Saleh *1, Kerolous Momtaz Yacoub2, Ahmed Salaheldin2 1Biomedical Engineering Department, Higher Institute of Engineering, ElShorouk Academy, Cairo, Egypt 2Systems and Biomedical Engineering Department Higher Institute of Engineering, El Shorouk Academy |
| Abstract: |
| One of the issues related to pregnancy is gestational diabetes mellitus (GDM). The risk of exploration in GDM is lowered with early detection. The study aims to develop a machine learning-based paradigm for the detection of GDM. Four distinct classifiers-logistic regression, decision tree, gradient boosting, and support vector machine were used. Nine factors of the clinical setting are distinguished in each case. A data set called PIMA for diabetes is used as the data source. The challenge of the existence of missing values is solved by substituting the mean and median values alternatively. Therefore, three scenarios have been applied to the dataset. The performance of the classifiers was assessed by measuring accuracy, sensitivity, specificity, and precision. Results yielded reliable outcomes for the proposed paradigm. In specific, the best results were obtained for the gradient boosting classifier by substituting the missing value with the median value. Results demonstrate the effectiveness of the paradigm. |
| Keywords: |
| gestational diabetes mellitus, pregnancy, machine learning, gradient boosting |
| Status : Paper Accepted |
