Predicting Hypoglycemia in Diabetic Patients Using Time-Sensitive Artificial Neural Networks

Predicting Hypoglycemia in Diabetic Patients Using Time-Sensitive Artificial Neural Networks

Khouloud Safi Eljil, Ghassan Qadah, Michel Pasquier
DOI: 10.4018/978-1-7998-0414-7.ch069
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Type-One Diabetes Mellitus (T1DM) is a chronic disease characterized by the elevation of glucose levels within patient's blood. It can lead to serious complications including kidney and heart diseases, stroke, and blindness. The proper treatment of diabetes, on the other hand, can lead to a normal longevity. Yet such a treatment requires tight glycemic control which increases the risk of developing hypoglycemia; a sudden drop in patients' blood glucose levels that could lead to coma and possibly death. Continuous Glucose Monitoring (CGM) devices placed on a patient body, measure glucose levels every few minutes. These devices can also detect hypoglycemia. Yet detecting hypoglycemia sometimes is too late for a patient to take proper action, so a better approach is to predict the hypoglycemic events ahead of time and alarm the patient to such occurrences. In this research, the authors develop a system that involves a special type of Artificial Neural Networks (ANN), the Time-Sensitive ANN (TS-ANN), to predict hypoglycemia events ahead of time and within a prediction horizon of thirty minutes. This period should be long enough to enable diabetic patients to avoid hypoglycemia by taking a proper action. A TS-ANN based system that is able to predict hypoglycemia events have been developed and tested with high accuracy results (average specificity of 98.2%, average accuracy of 97.6% and average sensitivity of 80.19% with a maximum value reaching 93%).
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2. State Of The Art In Glocuse And Hypoglycemia Prediction

During the last decade, plenty of research efforts went into developing machine learning algorithms suitable for predicting events in the healthcare field. Two types of research can be identified, namely, blood glucose predication and hypoglycemic events predication. Both of these research types are presented next.

Table 1.
A sample of blood glucose prediction studies using machine learning techniques
Study ReferenceYearNumber of Involved PatientsInput VariablesMachine Learning MethodPerformance RMSE
Georga, Fotiadis, & Protopappas, 2011 20117CGM data, insulin, carbohydrateSVR16.02
Pérez-Gandía, et al., 2010 20109CGM dataANN17.45 for group I
Pappada, Cameron, & Rosman200818CGM data, insulin, carbohydrate and othersANNVaried results

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