High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Shuai Song; Qiaoyun Wang; Xin Zou; Zhigang Li; Zhenhe Ma; Daying Jiang; YongQing Fu; Qiang Liu

doi:10.1016/j.saa.2023.123176

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 15:303:123176. doi: 10.1016/j.saa.2023.123176. Epub 2023 Jul 20.

Authors

Shuai Song¹, Qiaoyun Wang², Xin Zou¹, Zhigang Li¹, Zhenhe Ma¹, Daying Jiang³, YongQing Fu⁴, Qiang Liu⁵

Affiliations

¹ College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China.
² College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China. Electronic address: wangqiaoyun@neuq.edu.cn.
³ Zhongyou BSS (Qinhuangdao) Petropipe Company Limited, Qinhuangdao 066004, China.
⁴ Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
⁵ College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.

PMID: 37494812
DOI: 10.1016/j.saa.2023.123176

Abstract

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

Keywords: Artificial Bee Colony algorithm; Bagging algorithm; Blood glucose; Extreme Learning Machine; Raman spectroscopy.

MeSH terms

Algorithms
Blood Glucose*
Fourier Analysis
Humans
Machine Learning
Neural Networks, Computer*
Spectrum Analysis, Raman

Substances

Blood Glucose