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

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2023-12, Vol.303, p.123176, Article 123176
• Main Authors: Song, Shuai, Wang, Qiaoyun, Zou, Xin, Li, Zhigang, Ma, Zhenhe, Jiang, Daying, Fu, YongQing, Liu, Qiang
• Format: Article
• Language: English
• Subjects: Artificial Bee Colony algorithm; Bagging algorithm; Blood glucose; Extreme Learning Machine; Raman spectroscopy
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2023.123176

[Display omitted] •A method for rapid and accurate detection of human blood glucose concentration.•The method is based on Fourier transform Raman spectroscopy and machine learning.•We propose a novel ensemble extreme learning machine model.•The proposed model can achieve higher prediction accuracy and stability.•R2 and RMSEP are better than other methods. 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.