Hypoglycemia prediction using machine learning models for patients with type 2 diabetes

J Diabetes Sci Technol. 2015 Jan;9(1):86-90. doi: 10.1177/1932296814554260. Epub 2014 Oct 14.


Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. We validated our model using multiple data sets. In addition, we trained a second model, which used patient SMBG values and information about patient medication administration. The optimal number of SMBG values needed by the model was approximately 10 per week. The sensitivity of the model for predicting a hypoglycemia event in the next 24 hours was 92% and the specificity was 70%. In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%. Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models-which have been validated retrospectively and if implemented in real time-could be useful tools for reducing hypoglycemia in vulnerable patients.

Keywords: hypoglycemia prediction; machine learning; type 2 diabetes.

Publication types

  • Validation Study

MeSH terms

  • Algorithms*
  • Blood Glucose Self-Monitoring / standards
  • Blood Glucose Self-Monitoring / statistics & numerical data
  • Computer Simulation
  • Diabetes Mellitus, Type 2 / blood*
  • Humans
  • Hypoglycemia / blood*
  • Hypoglycemia / diagnosis*
  • Machine Learning*
  • Prognosis
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity