Extreme Learning Machine Algorithm Based on IPSO for Prediction of Blood Glucose Concentration in Patients with Type 1 Diabetes

Aiming at the complexity and instability of blood glucose data of diabetic patients, this paper introduces an extreme learning machine algorithm (ELM) based on improved particle swarm optimization (IPSO) into the prediction of blood glucose concentration in patients with type I diabetes. First, the blood glucose concentration time series of diabetes patients collected by dynamic blood glucose monitoring is smoothed and normalized to improve the smoothness of the blood glucose data sequence and weaken the randomness of the original blood glucose data sequence. Then, the extreme learning machine is optimized, the improved particle swarm optimization algorithm is introduced to select the appropriate parameters required by the extreme learning machine, and the optimized algorithm is applied to the prediction of blood glucose concentration of diabetic patients. The experimental results show that the extreme learning machine algorithm based on improved particle swarm optimization has higher accuracy for short-term blood glucose concentration prediction of patients.