A Hybrid Dynamic Wavelet-Based Modeling Method for Blood Glucose Concentration Prediction in Type 1 Diabetes

Abstract

Background: Diabetes mellitus (DM) is a chronic disease that affects public health. The prediction of blood glucose concentration (BGC) is essential to improve the therapy of type 1 DM (T1DM). Methods: Having considered the risk of hyper- and hypo-glycemia, we provide a new hybrid modeling approach for BGC prediction based on a dynamic wavelet neural network (WNN) model, including a heuristic input selection. The proposed models include a hybrid dynamic WNN (HDWNN) and a hybrid dynamic fuzzy WNN (HDFWNN). These wavelet-based networks are designed based on dominant wavelets selected by the genetic algorithm-orthogonal least square method. Furthermore, the HDFWNN model structure is improved using fuzzy rule induction, an important innovation in the fuzzy wavelet modeling. The proposed networks are tested on real data from 12 T1DM patients and also simulated data from 33 virtual patients with an UVa/Padova simulator, an approved simulator by the US Food and Drug Administration. Results: A comparison study is performed in terms of new glucose-based assessment metrics, such as gFIT, glucose-weighted form of ESODn (gESOD(n)), and glucose-weighted R-2 (gR(2)). For real patients' data, the values of the mentioned indices are accomplished as gFIT = 0.97 +/- 0.01, gESODn = 1.18 +/- 0.38, and gR(2) = 0.88 +/- 0.07. HDFWNN, HDWNN and jump NN method showed the prediction error (root mean square error RMSE) of 11.23 +/- 2.77 mg/dl, 10.79 +/- 3.86 mg/dl and 16.45 +/- 4.33 mg/dl, respectively. Conclusion: Furthermore, the generalized estimating equation and post hoc tests show that proposed models perform better compared with other proposed methods.