Blood glucose concentration prediction based on kernel canonical correlation analysis with particle swarm optimization and error compensation

Blood glucose levels in humans change over time. Continuous glucose monitoring system (CGMS), can constantly monitor the change of blood glucose concentration. Given the historical data of blood glucose, predicting the trend of blood glucose in a short term is important for diabetes. Appropriate behaviors can be adopted to prevent hypoglycemia or hyperglycemia.

The method proposed in this paper only uses historical blood glucose data as input, rather than complex multi-dimensional input. Previous articles have demonstrated that canonical correlation analysis (CCA) can effectively predict blood glucose. The linear relationship between historical blood glucose values and predicted values was only considered regrettably. To compensate for this, this paper adds a kernel function to find out the non-linear relationship between blood glucose. In the introduced kernel function, some parameters need to be adjusted. To reduce the deviation caused by manual parameter adjustment, this paper discusses the role of particle swarm optimization (PSO). Besides, this article puts forward an error compensation for CCA to enhance the precision. Finally based on the prediction results of PSO-KCCA, a personalized hypoglycemic warning threshold is proposed.

The proposed method is validated using clinical data by the root mean square error (RMSE) and differential coefficient (R²). The average RMSE result in PSO-KCCA was 8.01, 11.98, 12.45, 13.23, 14.53, 16.40 mg/dL in prediction horizon (PH) =5, 10, 15, 20, 25, 30 min. The average R² was 0.95, 0.95, 0.98, 0.97, 0.98, and 0.97, respectively. The CCA with error compensation (EC-CCA) reduced RMSE by 33.45% compared with CCA. For the hypoglycemic warning, the average sensitivity obtained at 6 different PH values was 94.37%, and the specificity was 92.25%.

The experimental results confirm the effectiveness of PSO-KCCA in blood glucose prediction. The proposed EC-CCA successfully reduces the delay in the time series prediction. The personalized hypoglycemic warning threshold consider the influence of the model accuracy on the prediction results. This method guarantees the rate of underreporting during monitoring and ensures patient safety.