Type 2 diabetes screening test by means of a pulse oximeter
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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%-61.1%]), with a specificity=64% for a threshold that gave a sensitivity=65%. We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than five minutes.
CitationMonte, E., Anyo, M., Torres, M., Juarez, P., Núñez, P., Aragón, C., Pedrosa, M., Álvarez-Rodríguez, M., González-Burguillos, M. Type 2 diabetes screening test by means of a pulse oximeter. "IEEE transactions on biomedical engineering", Vol. 64 (2). Feb 2017.