Type 2 diabetes screening test by means of a pulse oximeter.

In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts; the first analyses the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e. whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of 1; 157 subjects (two samples per subject) collected from five community health centres. The mean receiver operating characteristic (ROC) area found was 69:4% (median value 71:9% and range [75:4%