Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning


Autoři: Michael Mayo aff001;  Lynne Chepulis aff002;  Ryan G. Paul aff002
Působiště autorů: Department of Computer Science, University of Waikato, Hamilton, New Zealand aff001;  Waikato Medical Research Center, University of Waikato, Hamilton, New Zealand aff002;  Waikato Regional Diabetes Service, University of Waikato, Hamilton, New Zealand aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225613

Souhrn

Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.

Klíčová slova:

Blood sugar – Decision tree learning – Decision trees – Hypoglycemia – Hypoglycemics – Machine learning – Machine learning algorithms – Polynomials


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Článek vyšel v časopise

PLOS One


2019 Číslo 12