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

Author:

Shiva Shankar Reddy ,Gadiraju Mahesh,Preethi N. Meghana,Rao V.V.R.Maheswara

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.  

Publisher

European Alliance for Innovation n.o.

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software

Reference36 articles.

1. Melissa CS. Gestational Diabetes Signs, Symptoms, Test, Treatment, Complications, and Diet [online]. Medicine Net; [cited 2020 nov 22]. Available from: https://www.medicinenet.com/gestational_diabetes/article.htm

2. Jenna F. What are the symptoms of gestational diabetes? [online]. Medical News Today; [cited 2020 nov 22]. Available from: https://www.medicalnewstoday.com/articles/325177.

3. Mayo Clinic Staff. Gestational Diabetes [online]. Mayoclinic; [cited 2020 nov 22]. Available from: https://www.mayoclinic.org/diseases-conditions/gestational-diabetes/symptoms-causes/syc-20355339.

4. Glucose screening tests during pregnancy [online]. Medline Plus; [cited 2020 nov 22]. Available from: https://medlineplus.gov/ency/article/007562.htm.

5. Rohit G. 7 Types of Classification Algorithms [online]. Analytics India Magazine; [cited 2020 nov 22]. Available from: https://analyticsindiamag.com/7-types-classification-algorithms/.

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