Journal
SENSORS
Volume 20, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/s20061705
Keywords
artificial neural network; hypoglycemia; machine learning; support vector machine; type 1 diabetes; multiple daily injections; continuous glucose monitoring
Funding
- Spanish Government [DPI2016-78831-C2-2-R]
- National Council of Technological and Scientific Development, CNPq-Brazil [202050/2015-7, 207688/2014-1]
- Hospital Clinic i Universitari of Barcelona
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(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.
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