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Microsystems, Electronics and Acoustics
ISSN 2523-4447 · e‑ISSN 2523-4455 Open Access · CC BY-SA 4.0
Vol. 26 · Issue 2 · 2021 Aug 31, 2021 Electronic Systems and Signals

Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods

MB
Marko Romanovych Basarab Corresponding Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського» Ukraine
EI
Ekateryna Olehivna Ivanko Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського» Ukraine
VK
Vishwesh Kulkarni Інженерна школа, Університет Ворика,https://ror.org/01a77tt86 United Kingdom
Pages228845-1 - 228845-9 PublishedAug 31, 2021
PDF (Ukrainian)
VOL 26 · ISS 2 · 2021 View issue

Abstract

The paper is devoted to the application of machine learning methods to the prediction of the development of gestational diabetes mellitus in early pregnancy. Based on two publicly available databases, study assesses influence of such features as body mass index, thickness of triceps skin folds, ultrasound measurements of maternal visceral fat, first measured fasting glucose, and others a predictors of gestational diabetes mellitus. The supervised machine learning methods based on decision trees, support vector machines, logistic regression, k-nearest neighbors classifier, ensemble learning, Naive Bayes classifier, and neural networks were implemented to determine the best classification models for computerized gestational diabetes mellitus disease prediction. The accuracy of the different classifiers was determined and compared. Support vector machine classifier demonstrated the highest accuracy (83.0% of total correctly prognosed cases, 87.9% for healthy class, and 78.1% for gestational diabetes mellitus) in predicting the development of gestational diabetes based on features from Pima Indians Diabetes Database. Extreme gradient boosting classifier performed the best, comparing to other supervised machine learning methods, for Visceral Adipose Tissue Measurements during Pregnancy Database. It showed 87.9% of total correctly prognosed cases, 82.2% for healthy class, and 93.6% for gestational diabetes mellitus).

Keywords

#gestational diabetes mellitus #diabetic fetopathy #machine learning #prediction

References

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