Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data

  • G. Geetha Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
  • J. Godwin Ponsam Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
  • K. Nimala Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India

Abstract

Diabetes stands as the most widely recognized acute disease globally, resulting in death when it is not treated in an appropriate manner and time. We have developed a closedloop control system that uses continuous glucose, carbohydrate, and physiological variable data to regulate glucose levels and treat hyperglycemia and hypoglycemia, as well as a hypoglycemia early warning module. Overall, the proposed models are effective at predicting a normal glycemic range from >70 to 180 mg/dl, hypoglycemic values of <70 mg/dl, and hyperglycemic value of 180 mg/dl blood sugar levels. We undertook a seven-day, day-and-night home study with 15 adults. Initially, we started with checking insulin levels after meal consumption, and later, we concentrated on how our system reacted to the physical activity of the patients. Evaluation was conducted based on performance parameters such as precision (0.87), recall (0.87), F-score (0.82), delay (26.5±3), and error size (1.14±2).

Keywords

CGM, fog computing hypoglycemia, hyperglycemia, Apriori algorithm,

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Published
Jun 18, 2024
How to Cite
GEETHA, G.; PONSAM, J. Godwin; NIMALA, K.. Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data. Computer Assisted Methods in Engineering and Science, [S.l.], v. 31, n. 2, p. 157–174, june 2024. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/500>. Date accessed: 18 july 2024. doi: http://dx.doi.org/10.24423/cames.2024.500.
Section
[CLOSED]Scientific Computing and Learning Analytics for Smart Healthcare Systems