Predicting risk of gestational diabetes mellitus through nearest neighbour classification

Abstract

Gestational diabetes mellitus is a specific type of diabetes which arises as a complication of pregnancy, and which can adversely affect both the mother and the child. Diagnosis of this condition is carried out through screening coupled with an oral glucose tolerance test; however, these prove to be quite expensive to carry out. Therefore, it would be ideal that a prior clinical risk assessment would filter out any individuals who are not at risk of acquiring this disease, thus preventing the need to perform these costly tests. The prediction of risk of gestational diabetes mellitus is here formulated as a binary classification problem, with nearest neighbour methods being quite popular in this area of study. The k-Nearest Neighbour, Fixed-Radius Nearest Neighbour and Kernel classifiers are applied to a dataset consisting of pregnant women from 11 Mediterranean countries. Binary logistic regression is also applied in order to compare its performance to that of nearest neighbour methods. The classification techniques are thus compared using various performance measures determining which methods are the best at predicting positive cases of gestational diabetes mellitus.