Multilayer perceptron application for diabetes mellitus prediction in pregnancy care

The human intelligence modeling by brain components simulation, such as neurons and their connections, is part of leading smart decision computing paradigms. In Health, artificial neural networks (ANN) have the capacity to adapt to uncertainty situations and learn even with inaccurate data. This paper presents the modeling and performance evaluation of an ANN-based technique, named multilayer perceptron (MLP), for gestational diabetes mellitus (GDM) prediction that is responsible for several severe complications and affects 3 to 7% of pregnancies worldwide. Results show that this approach reached a precision of 0.74, Recall 0.741, F-measure 0.741, and ROC area 0.779. These indicators show that this method is an excellent predictor of this disease. This contribution offers a computational intelligence (CI) tool capable of identifying risk cases during pregnancy and, thus, reduce possible sequels for both pregnant woman and fetus.