Intelligent type 2 diabetes modelling

Diabetes mellitus affected an estimated 171 million people in the year 2000. The number of diabetic patients is projected to increase to an alarming figure of 366 million by the year 2030, out of which 90 – 95% of them are expected to be type 2 diabetes mellitus (T2DM) patients. The research presented in this report has attempted to go beyond the present insulin therapy of manual insulin infusion. The T2DM model that simulates the body reaction of a T2DM patient had been developed using real human clinical data that uses insulin pump therapy. Although the model is imperfect, it can still be applied to the simulation of a T2DM patient's blood glucose level. The new system which is proposed in this research uses closed-loop control together with fuzzy gain scheduling and recurrent self-evolving Takagi–Sugeno–Kang fuzzy neural network. Such a system will help the patient remove the need for manual insulin infusion. This proposed system will record the blood glucose level and predict the next iteration’s blood glucose level. The change in blood glucose level will help detect the food intake (carbohydrates) with reference to the gain scheduler and the controller will communicate with the insulin pump to infuse the corresponding amount of insulin. The system design is in its initial stages of testing and verification. A better understanding on the dynamics of T2DM is also needed to improve the system. However, it has provided significant information which lays the foundations for future research on insulin infusion without human intervention.Bachelor of Engineering (Computer Science