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

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).