Controlling blood glucose levels in diabetics by neural network predictor.
In this study we develop a system that uses some variables such as, level of exercise, stress, food intake, injected insulin and blood glucose level in previous intervals, as input and accurately predicts the blood glucose level in the next interval. The system is split up to make separate prediction of blood glucose level in the morning, afternoon, evening and night, using data from one patient covering a period of 77 days. We have used RBF neural network, and compared our result with MLP neural network that was implemented by the others. The assessment of the analysis resulted in a Root Mean Square Error of (0.04+/-0.0004) mmol/l.
Baghdadi
Golnaz
G
Biomedical Engineering. Dept., Shahed University, Tehran, Iran. baghdadi@shahed.ac.ir
Nasrabadi
Ali Motie
AM
D000465:Algorithms; D001185:Artificial Intelligence; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D020000:Decision Support Systems, Clinical; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000628:therapy*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D013813:Therapy, Computer-Assisted / Q000379:methods*; D016896:Treatment Outcome
D001786:Blood Glucose