Study on non-invasive blood glucose concentration prediction model based on LSTM

Study on non-invasive blood glucose concentration prediction model based on LSTM | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 August 2025 V1 Latest version Share on Study on non-invasive blood glucose concentration prediction model based on LSTM Authors : Qi Zhao 0009-0002-9291-2025 , Zhaoxia Liu , Xiongtao Yang , and Yan Wang [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175431323.32527316/v1 232 views 112 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To address the limitations of current glucose monitoring devices—such as inaccuracies and the burden of frequent measurements—this study introduces a cutting-edge deep learning approach using Long Short-Term Memory (LSTM) networks to predict blood glucose levels. By harnessing 13 days of historical blood glucose data, the model is capable of accurately forecasting glucose levels for the subsequent 13 days. This powerful predictive capability reduces the need for continuous glucose monitoring, minimizes the frequency of invasive tests, and significantly enhances patient comfort, all while ensuring effective glycemic control. The proposed LSTM model demonstrates remarkable potential in tracking glucose fluctuations, positioning it as a game-changing tool for diabetes management. Impressively, the model achieves an average prediction error of just 0.0007 when compared to invasive measurements, underscoring its extraordinary accuracy. This breakthrough represents a significant leap forward in non-invasive blood glucose detection, particularly when coupled with near-infrared (NIR) technology, offering unprecedented reliability and convenience for both patients and healthcare providers. Supplementary Material File (manuscript anonymous.docx) Download 1.59 MB Information & Authors Information Version history V1 Version 1 04 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords biomedical measurement blood Authors Affiliations Qi Zhao 0009-0002-9291-2025 Sichuan Cancer Hospital and Institute View all articles by this author Zhaoxia Liu Sichuan Cancer Hospital and Institute View all articles by this author Xiongtao Yang Sichuan Cancer Hospital and Institute View all articles by this author Yan Wang [email protected] Sichuan Cancer Hospital and Institute View all articles by this author Metrics & Citations Metrics Article Usage 232 views 112 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Qi Zhao, Zhaoxia Liu, Xiongtao Yang, et al. Study on non-invasive blood glucose concentration prediction model based on LSTM. Authorea . 04 August 2025. 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