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

Bharath Sudharsan, Malinda Peeples, Mansur Shomali

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Background: 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. Method: 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. Results: 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%. Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)86-90
Number of pages5
JournalJournal of Diabetes Science and Technology
Issue number1
StatePublished - Jan 1 2015


  • Hypoglycemia prediction
  • Machine learning
  • Type 2 diabetes

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Bioengineering
  • Biomedical Engineering


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