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

EAI Endorsed Transactions on Scalable Information Systems

article

Shiva Shankar Reddy¹  Mahesh Gadiraju¹  N. Meghana Preethi¹  V.V.R.Maheswara Rao² 

[1] Sagi Rama Krishnam Raju Engineering College;Shri Vishnu Engineering College for Women

关键词: 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

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

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.

【 授权许可】

CC BY