Abstract:
Diabetes Mellitus is a metabolic disease that causes the body to lose control over blood
glucose regulation. Patients with Type 1 diabetes completely rely on insulin therapy by
themselves or using some automated insulin delivery systems. In both the cases, it is
pertinent to have good estimate of future blood glucose levels. An efficient diabetes
management demands accurate prediction of future blood glucose levels, failure of which
results in short and long term health complications. With the modern exordium of
quantified-self such as continuous glucose monitoring(CGM) systems, a patient can have
access to their personalized glycemic profile which can be utilized for accurate prediction
of future blood glucose levels. In recent years, machine learning methodologies have
sparked a lot of interest in predicting glucose levels in diabetic patients, leading to the
development of a variety of methods and techniques. However, the prediction accuracies
of these methods are not good enough to be declared them as reliable predictors for
evaluating glycemic conditions. In this research work we utilized multi-layered Long Short
Term Memory(LSTM) network a famous deep learning technique based on recurrent
neural network(RNN) for making prediction of blood glucose levels in patients with type
1 diabetes. The proposed framework predicts the future blood glucose level using Ohio
T1DM dataset at prediction horizon(PH) of 30 and 60 minutes. Experimentation was also
carried out on better feature representation to model in order to achieve higher prediction
accuracy. The effect of different input feature sets, towards improvement of prediction
accuracy was also been investigated. The results on Ohio T1DM Dataset (2018), that
contain eight weeks’ worth of data shows that our method achieves the lowest RMSE score
of 14.76mg/dL and 25.48mg/dL for prediction horizon of 30min and 60min respectively.
The obtained results are the best known as per our knowledge using this dataset. The
proposed methodology can be utilized in closed loop systems for precise insulin delivery
to patient for their better glycemic control.
