Research Article
A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors
@ARTICLE{10.4108/eetsis.v10i3.2697, author={Shiva Shankar Reddy and Mahesh Gadiraju and N. Meghana Preethi and V.V.R.Maheswara Rao}, title={A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={3}, publisher={EAI}, journal_a={SIS}, year={2023}, month={1}, keywords={Gestational diabetes mellitus (GDM), Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbor (KNN), ID3, CART, J48, k-fold cross-validation}, doi={10.4108/eetsis.v10i3.2697} }
- Shiva Shankar Reddy
Mahesh Gadiraju
N. Meghana Preethi
V.V.R.Maheswara Rao
Year: 2023
A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors
SIS
EAI
DOI: 10.4108/eetsis.v10i3.2697
Abstract
Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.
Copyright © 2022 Shiva Shankar Reddy et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.