Diabetes is one of the most widespread diseases around the world, especially in the western world where non-healthy and fast foods are widely used. Many types of research have been investigated for developing methods for predicting, diagnosing, and treating diabetes. One of the tools which are used for this purpose is mathematical modeling which is used for developing models of blood glucose and insulin intakes. In this paper, a model to determine the proper insulin dose for diabetic inpatients was implemented using Artificial Neural Network (ANN). The model developed by taking in consideration ten different parameters (Patient's Gender, Patient's Age, Body Mass Index for Patient, Disease History, Total Daily Insulin Doses, Diabetes Type, Smoking Factor, Genetic Factor, Creatinine Clearance, and Accumulative Glucose) in addition to real time blood glucose readings. The model developed based on a data set from 159 inpatients from three different hospitals, and it was found that the model with the best performance was based one hidden layer with six neurons and seven inputs. The significant inputs were glucose readouts, glucose difference, normal range, accumulative glucose, history of the disease, total insulin dose, and the patient's gender. The MSE of the best model was 5.413 and the correlations was 0.9315 with negligible training time.
Key words: Neural Networks, Insulin Dose, Glucose Level, Modelling
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