Predicting Blood Glucose in Type-1 Diabetic Patients using Deep Learning Techniques - Approaches and Challenges

Abstract

Diabetes Mellitus (DM) is a metabolic disorder where the body fails to produce the digestive hormone insulin, or the body’s ability to respond to insulin is limited. This situation leads to abnormal metabolism of carbohydrates and elevated blood sugar level. Type-1 diabetes (T1DM) is a condition arises mainly due to auto immunity disorder in which the immunity cells of the body mistakenly destroy the beta cells in the pancreas, which produce insulin. T1DM patients require to have proper control of their blood glucose level through proper medication, physical activities and continuous monitoring of blood glucose levels. A wide range of advanced wellbeing innovations, particularly computerized applications, have been growing quickly to assist individuals with dealing with their diabetes. Artificial Intelligence is a rapidly growing field, and its applications to diabetes research are becoming significantly more quickly. This paper is a review of six studies of existing neural networkbased models for the prediction of future blood glucose level in T1DM patients and describes some challenges to predict future blood glucose with the available data. These models include prediction of blood glucose level using Convolutional Neural Networks (CNN), Feedforward Neural Network (FNN), Recurrent Neural Network (RNN) implemented using Long Short Term Memory (LSTM), Convolutional Recurrent Neural Network (CRNN), Bidirectional LSTM (BiLSTM) and Dilated Recurrent Neural Networks (DRNN)