Blood Glucose Concentration Prediction Based on Double Decomposition and Deep Extreme Learning Machine Optimized by Nonlinear Marine Predator Algorithm

Yang Shen, Deyi Li, Wenbo Wang () and Xu Dong
Additional contact information
Yang Shen: College of Science, Wuhan University of Science and Technology, Wuhan 430065, China
Deyi Li: College of Science, Wuhan University of Science and Technology, Wuhan 430065, China
Wenbo Wang: Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, China
Xu Dong: College of Science, Wuhan University of Science and Technology, Wuhan 430065, China

Mathematics, 2024, vol. 12, issue 23, 1-25

Abstract: Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decomposition on prediction accuracy. To enhance the prediction accuracy, a new short-term glucose prediction model that integrates the double decomposition technique, nonlinear marine predator algorithm (NMPA) and deep extreme learning machine (DELM) is proposed. First of all, the initial blood glucose data are decomposed by variational mode decomposition (VMD) to reduce its complexity and non-stationarity. To make full use of the decomposed residual component, the time-varying filter empirical mode decomposition (TVF-EMD) is utilized to decompose the component, and further realize complete decomposition. Then, the NMPA algorithm is utilized to optimize the weight parameters of the DELM network to avoid any fluctuations in prediction performance, and all the decomposed subsequences are predicted separately. Finally, the output results of each model are superimposed to acquire the predicted value of blood sugar concentration. Using actual collected blood glucose concentration data for predictive analysis, the results of three patients show the following: (i) The double decomposition strategy effectively reduces the complexity and volatility of the original sequence and the residual component. Making full use of the important information implied by the residual component has the best decomposition effect; (ii) The NMPA algorithm optimizes DELM network parameters, which can effectively enhance the predictive capabilities of the network and acquire more precise predictive results; (iii) The model proposed in this paper can achieve a high prediction accuracy of 45 min in advance, and the root mean square error values are 5.2095, 4.241 and 6.3246, respectively. Compared with the other eleven models, it has the best prediction accuracy.

Keywords: blood glucose concentration; double decomposition; nonlinear marine predator algorithm; deep extreme learning machine; prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3708/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3708/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3708-:d:1530116

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().