ArticleIEEE access : practical innovations, open solutions2021
Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 115 citations in OpenAlex.
- A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management.Biosensors · 2022Pooled it
- Ni-Cu-Zn Prussian Blue Analogue Nanozyme-Based Cascade Colorimetric Platform for Glucose Detection.Journal of fluorescence · 2026Article
- Review
- Matrix-Aware Electrochemical Sensing: A Neural Network Approach to Deciphering Medium-Specific Surface Dynamics.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Non-Invasive Technologies in Wearable Glucose Monitoring: A Structured Overview for the Future.Biosensors · 2026Review
- Noninvasive Blood Glucose Monitoring with Machine Learning Enhanced Transmittance Spectroscopy.ACS omega · 2026Article
- Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications.Journal of advanced research · 2026Review
- A Single-Wavelength Near-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- High-Sensitivity and Temperature-Robust Gas Sensor Based on Magnetically Induced Differential Mode Splitting in InSb Photonic Crystals.Sensors (Basel, Switzerland) · 2026Article
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Non invasive blood glucose estimation using green light photoplethysmography and machine learning.Frontiers in digital health · 2026Article
- Advancing the Development of Subcutaneous Glucose Biosensors: Cargo-Carrying Adhesive Biosensor Systems (CABs).Macromolecular bioscience · 2026Article
- Noninvasive subterahertz glucose monitoring using a communication inspired eye diagram.Scientific reports · 2025Article
- Artificial Intelligence-Based Wearable Sensing Technologies for the Management of Cancer, Diabetes, and COVID-19.Biosensors · 2025Review
- Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning.Scientific reports · 2025Article
- Review
- The Application of Kernel Ridge Regression for the Improvement of a Sensing Interferometric System.Sensors (Basel, Switzerland) · 2025Article
- AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.Journal of medical Internet research · 2024Article
- Classification of glucose-level in deionized water using machine learning models and data pre-processing technique.PloS one · 2024Article
- Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.Frontiers in neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.