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


  • 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




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


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.



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

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.

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.

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

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

AmitabhaDey. Machine Learning (ML) - Data Pre processing[online]. , Data Driven Investor; [cited 2020 nov 22]. Available from: https://medium.com/datadriveninvestor/data-preprocessing-for-machine-learning-188e9eef1d2c.

RenuKhandelwal. K fold and other cross-validation techniques[online]. , Data Driven Investor; [cited 2020 nov 22]. Available from: https://medium.com/datadriveninvestor/k-fold-and-other-cross-validation-techniques-6c03a2563f1e.

Pima Indians Diabetes Database [online]. data.world; [cited 2020 nov 22]. Available from: https://data.world/data-society/pima-indians-diabetes-database.

Appaji SV, Shankar RS, Murthy KV, Rao CS. Cardiotocography Class Status Prediction Using Machine Learning Techniques. Indian Journal of Public Health Research & Development. 2019;10(8):651-7.

Vasamsetty CS, Peri SR, Rao AA, Srinivas K, Someswararao C. Gene Expression Analysis31for Type-2 Diabetes Mellitus-A Case Study on Healthy vs Diabetes with ParentalHistory. International Journal of Engineering and Technology. 2011 Jun 1;3(3):310-314.

Vasamsetty CS, Peri SR, Rao AA, Srinivas K, Someswararao c. Gene Expression Analysis for Type-2 Diabetes Mellitus--A Study on Diabetes with and without Parental History. Journal of Theoretical & Applied Information Technology. 2011 May 15;27(1):43-53.

Geetha VR, Jayaveeran, N. Comparative analysis of gestational diabetes using data mining techniques. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2018; 3(8): 2456-3307

Mala SJ. A Hybrid Apporach of Classification Techniques for Predicting Diabetes using Feature Selection. International Journal of Trend in Scientific Research and Development.2019;3(5):2506-2510.

Koklu M, Unal Y. Analysis of a population of diabetic patients databases with classifiers. International Journal of Biomedical and Biological Engineering. 2013 Aug 22;7(8):481-3.

Kadhm MS, Ghindawi IW, Mhawi DE. An accurate diabetes prediction system based on K-means clustering and proposed classification approach. International Journal of Applied Engineering Research. 2018; 13(6):4038-41.

Kandhasamy JP, Balamurali SJ. Performance analysis of classifier models to predict diabetes mellitus. Procedia Computer Science. 2015 Jan 1;47:45-51.

Prema NS, Pushpalatha MP. Evaluation of Risk Factors of Gestational Diabetes Mellitus (GDM) using Data Mining. International Journal of Engineering and Advanced Technology. 2019; 8(6): 695-698.

Renuka DM, Shyla JM. Analysis of Various Data Mining Techniques to Predict Diabetes Mellitus. Int. J. Appl. Eng. Res. 2016;11(1):727-730.

Rithesh RN. SVM-KNN: a novel approach to classification based on svm and knn. International Research Journal of Computer Science. 2017;4(8): 43-49.

Suryakirani, RS and Porkodi, R. Comparative Study and Analysis of Classification Algorithms in Data Mining Using Diabetic Dataset. IJSRST. 2018; 4(2):299-304.

Saradha S, Sujatha P. Prediction of gestational diabetes diagnosis using SVM and J48 classifier model. International Journal of Engineering & Technology. 2018;7(2.21):323-326.

Srideivanai N, Chandrasekaran RM. and Ramasubramanian P. Data Mining Techniques for Performance Evaluation of Diagnosis in Gestational Diabetes. International Journal of Current Research and Academic Review. 2014; 2(10):91-98

Sumangali K., Geetika, BSR. andHarshithaA.Author AA. A Classifier Based Approach for Early Detection of Diabetes Mellitus. In: Proceedings of the International Conf. on Control, Instrumentation, Communication and Computational Technologies; 2016; Kumaracoil, India. IEEE; 2016. p. 389-392.

Bashir S, Qamar U, Khan FH, Javed MY. An efficient rule-based classification of diabetes using ID3, C4. 5, & CART ensembles. In: Proceedings of the 12th International Conference on Frontiers of Information Technology; 2014 Dec 17-14; Islamabad, Pakistan. IEEE; 2014. p. 226-231.

Shankar RS, Gupta VM, Murthy KV, Rao CS. Breast cancer Data classification Using Machine Learning Mechanisms. Indian Journal of Public Health Research & Development. 2019;10(5):214-220.

Sathya S. and Rajesh A.An Effective Prediction of Diabetics using ID3 Classification Algorithm. Middle-East Journal of Scientific Research. 2016; 24: 207-211

Fu H, Cheng J, Xu Y and Liu J. Glaucoma detection based on deep learning network in fundus image. In: Deep learning and convolutional neural networks for medical imaging and clinical informatics;. Springer, Cham; 2019. p. 119-137.

Sanaa AEl. Classifying Datasets using some Different Classification Methods. International Journal of Engineering and Technical Research. 2016 ),.5(2):148-154.

Reddy SS, Rajender R, Sethi N. A data mining scheme for detection and classification of diabetes mellitus using voting expert strategy. International Journal of Knowledge-Based and Intelligent Engineering Systems. 2019 Jan 1;23(2):103-108.

Reddy SS, Sethi N, Rajender R. Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction. International Journal of Knowledge-based and Intelligent Engineering Systems. 2021 Jan 1;25(4):369-83.

Reddy SS, Sethi N, Rajender R. A Comprehensive Analysis of Machine Learning Techniques for Incessant Prediction of Diabetes Mellitus. International Journal of Grid and Distributed Computing. 2020;13(1):1-22.

Reddy SS, Sethi N, Rajender R. Mining of multiple ailments correlated to diabetes mellitus. Evolutionary Intelligence. 2021 Jun;14(2):733-40.

Reddy SS, Sethi N, Rajender R. A review of data mining schemes for prediction of diabetes mellitus and correlated ailments. In2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA) 2019 Sep 19 (pp. 1-5). IEEE.

Reddy SS, Sethi N, Rajender R. Evaluation of deep belief network to predict hospital readmission of diabetic patients. In2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) 2020 Jul 15 (pp. 5-9). IEEE.

Reddy SS, Sethi N, Rajender R. Safe Prediction of Diabetes Mellitus Using Weighted Conglomeration of Mining Schemes. In2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) 2020 Nov 5 (pp. 1213-1220). IEEE.

Reddy SS, Sethi N, Rajender R, Vetukuri V. Non-invasive Diagnosis of Diabetes Using Chaotic Features and Genetic Learning. InInternational Conference on Image Processing and Capsule Networks 2022 (pp. 161-170). Springer, Cham.




How to Cite

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