A comparative study of classifiers for early diagnosis of gestational Diabetes Mellitus

Year 2020, Volume: 69 Issue: 1, 754 - 770, 30.06.2020

Priya Shirley Muller M. Nirmala

https://doi.org/10.31801/cfsuasmas.704394

Abstract

Gestational Diabetes Mellitus (GDM), usually found deploying a medical test called the Oral Glucose Tolerance Test (OGTT), is a prevalent complication during pregnancy. Early detection of GDM and identifying the most influential risk factors of GDM pose to be a challenging problem and is found to be crucial as GDM has dreadful health indications for both mother and the baby. The performances of computational techniques like Radial Basis Function (RBF) neural network and Multilayer Perceptron Network (MLP) were collated with that of the statistical technique Discriminant Analysis (DA) on real time GDM datasets for diagnosis of GDM in multigravida pregnant women, specifically women who have been pregnant more than once, without even a visit to the hospital. The most influential risk factors were identified using DA while the overall performance of MLP beyond doubt established itself to be the most effective technique for early diagnosis of GDM in women during pregnancy.

Keywords

Gestational diabetes mellitus, classifier, risk factors, multilayer perceptron network, back propagation algorithm, radial basis function algorithm, discriminant analysis

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