Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data
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...
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Institute of Fundamental Technological Research Polish Academy of Sciences
2024-06-01
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| Series: | Computer Assisted Methods in Engineering and Science |
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| Online Access: | https://cames.ippt.pan.pl/index.php/cames/article/view/500 |
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| author | G. Geetha J. Godwin Ponsam K. Nimala |
| author_facet | G. Geetha J. Godwin Ponsam K. Nimala |
| author_sort | G. Geetha |
| collection | DOAJ |
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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).
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| format | Article |
| id | doaj-art-c682ae825a0b4599ac06953044d2dac8 |
| institution | Kabale University |
| issn | 2299-3649 2956-5839 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Computer Assisted Methods in Engineering and Science |
| spelling | doaj-art-c682ae825a0b4599ac06953044d2dac82025-08-20T03:28:47ZengInstitute of Fundamental Technological Research Polish Academy of SciencesComputer Assisted Methods in Engineering and Science2299-36492956-58392024-06-0131210.24423/cames.2024.500Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate DataG. Geetha0J. Godwin Ponsam1K. Nimala2Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, KattankulathurDepartment of Networking and Communications, School of Computing, SRM Institute of Science and Technology, KattankulathurDepartment of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur 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). https://cames.ippt.pan.pl/index.php/cames/article/view/500CGMfog computing hypoglycemiahyperglycemiaApriori algorithm |
| spellingShingle | G. Geetha J. Godwin Ponsam K. Nimala Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data Computer Assisted Methods in Engineering and Science CGM fog computing hypoglycemia hyperglycemia Apriori algorithm |
| title | Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data |
| title_full | Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data |
| title_fullStr | Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data |
| title_full_unstemmed | Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data |
| title_short | Noninvasive Blood Glucose Level Monitoring for Predicting Insulin Infusion Rate Using Multivariate Data |
| title_sort | noninvasive blood glucose level monitoring for predicting insulin infusion rate using multivariate data |
| topic | CGM fog computing hypoglycemia hyperglycemia Apriori algorithm |
| url | https://cames.ippt.pan.pl/index.php/cames/article/view/500 |
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