Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy

Anand, Pradeep Kumar; Shin, Dong Ryeol; Memon, Mudasar Latif

doi:10.3390/diagnostics10050285

Cited by 16 publications

(11 citation statements)

References 32 publications

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“…MARD was an indication of error rate scale between predicted glucose level and reference glucose level. Generally, a BG meter has an accuracy of 15% MARD 40 (supplementary material Method 3 , Sect. 7).…”

Section: Experimental Methodsmentioning

confidence: 99%

Subcutaneously implantable electromagnetic biosensor system for continuous glucose monitoring

Kim

Malik²,

Seo

et al. 2022

Sci Rep

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Continuous glucose monitoring systems (CGMS) are becoming increasingly popular in diabetes management compared to conventional methods of self-blood glucose monitoring systems. They help understanding physiological responses towards nutrition intake, physical activities in everyday life and glucose control. CGMS available in market are of two types based on their working principle. Needle type systems with few weeks lifespan (e.g., enzyme-based Freestyle Libre) and implant type system (e.g., fluorescence-based Senseonics) with few months of lifespan are commercially available. An alternate to both working methods, herein, we propose electromagnetic-based sensor that can be subcutaneously implanted and capable of tracking minute changes in dielectric permittivity owing to changes in blood glucose level (BGL). Proof-of-concept of proposed electromagnetic-based implant sensor has been validated in intravenous glucose tolerance test (IVGTT) conducted on swine and beagle in a controlled environment. Sensor interface modules, mobile applications, and glucose mapping algorithms are also developed for continuous measurement in a freely moving beagle during oral glucose tolerance test (OGTT). The results of the short-term (1 h, IVGTT) and long-term (52 h, OGTT) test are summarized in this work. A close trend is observed between sensor frequency and BGL during GTT experiments on both animal species.

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Section: Experimental Methodsmentioning

confidence: 99%

Subcutaneously implantable electromagnetic biosensor system for continuous glucose monitoring

Kim

Malik²,

Seo

et al. 2022

Sci Rep

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“…A personalized glucose monitoring system (PGMS) comprises both non-invasive and invasive sensors on a solitary device [ 156 ]. In one study, blood glucose data was used for training the ML models.…”

Section: The Application Of ML and Dl Models For The Management Predi...mentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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“…MARD shows how close is the measured value from the actual value. The smaller the percentage, the more accurate the device is [13,67].…”

Section: Regulations and Error Analysismentioning

confidence: 99%

“…The difference is that PEG analysis differentiates between type 1 diabetes and type 2 diabetes. Table 3 describes the risk assessment for each zone in CEG analysis and PEG analysis [5,13,67]. The performance of the non-invasive continuous blood glucose monitoring devices is usually validated against the invasive methods that are considered the gold standard.…”

Section: Regulations and Error Analysismentioning

confidence: 99%

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Alsunaidi

Althobaiti

Tamal

et al. 2021

Sensors

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The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

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Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy

Cited by 16 publications

References 32 publications

Subcutaneously implantable electromagnetic biosensor system for continuous glucose monitoring

Subcutaneously implantable electromagnetic biosensor system for continuous glucose monitoring

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

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