Predicting hypoglycemia in diabetic patients using data mining techniques

Abstract

The proper control of blood glucose levels in diabetic patients reduces serious complications. Yet tighter glycemic control increases the risk of developing hypoglycemia, a sudden drop in patients' blood glucose levels that causes coma and possibly death if proper action is not taken immediately. In this paper, we propose a hypoglycemia prediction model, using recent history of subcutaneous glucose measurements collected via Continuous Glucose Monitoring (CGM) sensors. The model is able to predict hypoglycemia events within a prediction horizon of thirty minutes accurately (sensitivity= 86.47%, specificity= 96.22, accuracy= 95.97%) using only the last two glucose measurements and the difference between them. More remarkably, this study shows the ability to develop a generalized prediction model suitable for predicting hypoglycemia events for the group of patients participating in the study.