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

PLoS One. 2019 Dec 2;14(12):e0225613. doi: 10.1371/journal.pone.0225613. eCollection 2019.


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.

MeSH terms

  • Blood Glucose / analysis*
  • Blood Glucose Self-Monitoring / instrumentation
  • Blood Glucose Self-Monitoring / methods*
  • Datasets as Topic
  • Diabetes Mellitus, Type 1 / blood
  • Diabetes Mellitus, Type 1 / drug therapy*
  • Forecasting
  • Humans
  • Hypoglycemia / blood
  • Hypoglycemia / chemically induced
  • Hypoglycemia / diagnosis*
  • Hypoglycemic Agents / administration & dosage
  • Hypoglycemic Agents / adverse effects
  • Insulin / administration & dosage
  • Insulin / adverse effects
  • Laboratory Critical Values
  • Pancreas, Artificial
  • Self Medication / adverse effects
  • Support Vector Machine*


  • Blood Glucose
  • Hypoglycemic Agents
  • Insulin

Grant support

The author(s) received no specific funding for this work.