Glucose Monitoring with AI Analytics for Diabetes Management Using Machine Learning and Smart Devices
AUTHORS
I. Contreras,University of Pretoria, South Africa
J. Vehi,University of Pretoria, South Africa
ABSTRACT
Over the past few years, there has been a noticeable growth in the usage of smart devices for diabetes treatment. These technologies are meant to make life easier for people with diabetes because they have the potential to improve the stability of blood sugar monitoring and predict the onset of dangerous episodes (hypo/hyperglycemia). Notwithstanding, the primary goals of diabetic self-management are to enhance the lifestyle and quality of life of those living with the disease. This study used the literature that addressed diabetes to conduct a systematic review to monitor and control the disease effectively. The search was narrowed to include topical keywords like Artificial Intelligence (AI), technology, self-management, and diabetes, for which PubMed databases were used. There were 2655 papers in all, released between 2013 and 2023. Predicting blood glucose, identifying risk events early, automatically adjusting insulin dosages, and other diabetes care issues are the main goals of most of the chosen research. Wearable technology and AI methods were combined in these investigations. Much scientific attention has been drawn to wearable technology like Continuous Glucose Monitoring (CGM) in treating chronic illnesses like diabetes. Not only may they help avoid diabetes-related problems, but they can also aid in managing diabetes. Utilizing these gadgets has also enhanced the quality of life and sickness treatment.
KEYWORDS
Continues glucose monitoring, Diabetes management, Artificial intelligence, Healthcare industry, Smart devices
REFERENCES
[1] S. Misra and N. S. Oliver, “Utility of ketone measurement in the prevention, diagnosis, and management of diabetic ketoacidosis,” Diabetic Med. vol.32, pp.14-23, (2015)
[2] R. A. Rahman, N. S. A . Aziz, and M. Kassim, “IoT-based personal health care monitoring device for diabetic patients,” In: ISCAIE 2017 – 2017 IEEE symposium on computer applications and industrial electronics, (ed. Ihsan Mohd Yassin), Langkawi, Malaysia, 24-25 April, no.17261459, pp.168-173, (2017)
[3] L. Catarinucci, “An IoT-aware architecture for smart healthcare systems,” IEEE Internet Things J, vol.2, pp.515-526, (2015)
[4] A. Murakami, “A continuous glucose monitoring system in critical cardiac patients in the intensive care unit,” In 2006 Computers in Cardiology, IEEE, pp.233-236, (2006)
[5] H. Ketabdar and M. Lyra, “System and methodology for using mobile phones in live remote monitoring of physical activities,” In Proceedings of the IEEE International Symposium on Technology and Society, Wollongong, NSW, Australia, pp.7-9, (2010)
[6] S. A. Siddiqui, A. Zhang, J. Lloret, H. Song, and Z. Obradovic, “Pain-free blood glucose monitoring using wearable sensors: Recent advancements and prospects,” IEEE Rev. Biomed. Eng. vol.11, pp.21-35
[7] M. Ali, L. Albasha, and H. Al-Nashash, “A Bluetooth low energy implantable glucose monitoring system,” Proceedings of the 41st European Microwave Conference, pp.10-13, Manchester, UK, (2011)
[8] P. ang, D. Schmidt, J. White, “Towards precision behavioral medicine with IoT: iterative design and optimization of a self-management tool for type 1 diabetes,” In: Proceedings - 2018 IEEE International Conference on Healthcare informatics, ICHI, 4-7 June, no.17956551, 2018, pp.64–74. New York, NY: IEEE, (2018)
[9] M. M. Alotaibi, R. S. H. Istepanian, and A. Sungoor, “An intelligent mobile diabetes management and educational system for Saudi Arabia: System architecture,” In: Proceedings of IEEE-EMBS International conference on biomedical and health informatics, BHI 2014, Valencia, Spain, June no.14484413, pp.29–32, New York, NY: USA, (2014)
[10] N. M. Saravana, T. Eswari, and P. Sampath, “Predictive methodology for diabetic data analysis in big data,” Procedia Comput, vol.50, pp.203-208, (2015)
[11] J. M. D. Perez, W. B. Misa, P. A. C., Tan, R. Yap, and J. A. Robles, “Wireless blood sugar monitoring system using ion-sensitive field effect transistor,” In Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore, pp.22-25 November, pp.1742-1746, (2016)
[12] M. A. Rahmat, E. L. M. Su, M. M. Addi, C. F. Yeong, and G. Qo, “IoT-based non-invasive blood glucose monitoring. J. Telecommun. Electron. Comput. Eng, vol.9, pp.71-75, (2017)
[13] A. Rghioui, J. Lloret, L. Parra, S. Sendra, A. Oumnad, “Glucose data classification for diabetic patient monitoring, Appl. Sci. vol.9, pp.4459, (2019)
[14] D. Moher, L. Shamseer, M. Clarke, D. Ghersi, A. Liberati, M. Petticrew, P. Shekelle, L. A. Stewart, “Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement,” Syst. Rev. vol.4, pp.1-9, (2015)
[15] E. Stefana, F. Marciano, D. Rossi, P. Cocca, and G. Tomasoni, “Wearable devices for ergonomics: A systematic literature review. Sensors, vol.21, pp.777, (2021)
[16] A. Liberati, D. G. Altman, J. Tetzlaff, C. Mulrow, P. C. Gøtzsche, J. P. Ioannidis, M. Clarke, P. J. Devereaux, J. Kleijnen, and D. Moher, “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration,” J. Clin. Epidemiol, vol.62, pp.1-e34, (2009)
[17] A. D. Association, “Diabetes technology: Standards of medical care in diabetes—2019,” Diabetes Care, vol.42, no.71-80, (2019)
[18] P. Sharma and A. P. R. Bhatia, “Implementation of decision tree algorithm to analysis the performance,” (2012), https://www.ijarcce.com/upload/december/24-Implementation%20of%20Decision.pdf
[19] A. Hazra, S. K. Mandal, and A. Gupta, “Study and analysis of breast cancer cell detection using Naïve Bayes, SVM and Ensemble Algorithms,” Int. J. Comput. Appl. vol.145, pp.39-45, (2016)
[20] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. vol.12, pp.2825-2830, (2011)
[21] C. S. Le and J. C. Van Houwelingen, “Ridge estimators in logistic regression,” J. R. Stat. Soc. Ser. (Appl. Stat.), vol.41, pp.191–201, (1992)
[22] R. Miotto, L. Li, B. A. Kidd, J. T. Dudley, “Deep patient: An unsupervised representation to predict the future of patients from the electronic health records,” Sci. Rep. vol.6, pp.1-10, (2016)
[23] L. A. C. Wright and I. B. Hirsch, “Metrics beyond hemoglobin A1C in diabetes management: Time in range, hypoglycemia, and other parameters,” Diabetes Technol. Ther. vol.19, no.16–S-26, (2017)
[24] A. Dagliati, S. Marini, L. Sacchi, G. Cogni, M. Teliti, V. Tibollo, C. P. De, L. Chiovato, and R. Bellazzi, “Machine learning methods to predict diabetes complications,” J. Diabetes Sci. Technol, vol.12, pp.295-302, (2018)
[25] T. Danne, R. Nimri, and T. Battelino, “International consensus on the use of continuous glucose monitoring,” Diabetes Care, vol.40, pp.1631-1640, (2017)
[26] E. Yom-Tov, G. Feraru, M. Kozdoba, S. Mannor, M. Tennenholtz, and I. Hochberg, “Encouraging physical activity in patients with diabetes: Intervention using a reinforcement learning system,” J. Med. Int. Res. vol.19, pp.338, (2017)
[27] C. Zecchin, A. Facchinetti, G. Sparacino, C. Cobelli, “Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information,” Comput. Methods Programs Biomed, vol.113, pp.144-152, (2014)
[28] N. Nuryani, S. S. Ling, and H. Nguyen, “Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection,” Ann. Biomed. Eng, vol.40, pp.934-945, (2012)
[29] M. Anthimopoulos, J. Dehais, S. Shevchik, B. H. Ransford, D. Duke, P. Diem, and S. Mougiakakou, “Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones,” J. Diabetes Sci. Technol., vol.9, pp.507–515, (2015)
[30] F. Allam, Z. Nossai, H. Gomma, I. Ibrahim, and M. A. Abdelsalam, “Recurrent neural network approach for predicting glucose concentration in type-1 diabetic patients,” In Engineering Applications of Neural Networks; Springer: Berlin/Heidelberg, Germany, pp.254-259, (2011)
[31] G. Cappon, A. Facchinetti, G. Sparacino, P. Georgiou, and P. Herrero, “Classification of postprandial glycemic status with application to insulin dosing in type 1 diabetes - An insulin proof-of-concept,” Sensors, vol.19, pp.3168, (2019)
[32] P. Stolfi, I. Valentini, M. C. Palumbo, P. Tieri, A. Grignolio, F. Castiglione, “Potential predictors of type-2 diabetes risk: Machine learning, synthetic data, and wearable health devices,” BMC Bioinform, vol.21, pp.1-19, (2020)
[33] C. W. Tsai, C. H. Li, R. W. K. Lam, C. K. Li, and S. Ho, “Diabetes care in motion: Blood glucose estimation using wearable devices,” IEEE Consum. Electron. Mag. vol.9, pp.30-34, (2019)
[34] A. Rghioui, J. Lloret, M. Harane, and A. Oumnad, “A smart glucose monitoring system for diabetic patients,” Electronics, vol.9, pp.678, (2020)
[35] M. Maritsch, S. Föll, V. Lehmann, C. Bérubé, M. Kraus, S. Feuerriegel, T. Kowatsch, T. Züger, C. Stettler, and E. Fleisch, “Towards wearable-based hypoglycemia detection and warning in diabetes,” In Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, pp.25-30, pp.1-8, (2020)
[36] R. Bunescu, N. Struble, C. Marling, J. Shubrook, and F. Schwartz, “Blood glucose level prediction using physiological models and support vector regression,” In Proceedings of the 2013 12th International Conference on Machine Learning and Applications, Miami, FL, USA, vol.1, pp.135-140, (2013)
[37] C. M. Bishop, “Neural networks for pattern recognition,” Oxford University Press: Oxford, UK, (1995)
[38] D. P. Kumar, T. Amgoth, and C. S. R. Annavarapu, “Machine learning algorithms for wireless sensor networks: A survey,” Inf. Fusion 2019, vol.49, pp.1-25, (2019)
[39] K. P. Murphy, “Machine learning: A probabilistic perspective,” MIT Press: Cambridge, MA, USA, (2012)