A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

Authors

  • Shiva Shankar Reddy Sagi Rama Krishnam Raju Engineering College (A), Bhimavaram, Andhrapradesh, INDIA
  • Mahesh Gadiraju Sagi Rama Krishnam Raju Engineering College (A), Bhimavaram, Andhrapradesh, INDIA
  • N. Meghana Preethi Sagi Rama Krishnam Raju Engineering College (A), Bhimavaram, Andhrapradesh, INDIA
  • V.V.R.Maheswara Rao Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhrapradesh, INDIA

DOI:

https://doi.org/10.4108/eetsis.v10i3.2697

Keywords:

Gestational diabetes mellitus (GDM), Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbor (KNN), ID3, CART, J48, k-fold cross-validation

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.

 

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Published

11-01-2023

How to Cite

1.
Shiva Shankar Reddy, Gadiraju M, Preethi NM, Rao V. A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors. EAI Endorsed Scal Inf Syst [Internet]. 2023 Jan. 11 [cited 2024 Apr. 25];10(3):e8. Available from: https://publications.eai.eu/index.php/sis/article/view/2697