Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Karisma Putra - Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Mahendro Prasetyo Kusumo - Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Prayitno Prayitno - Politeknik Negeri Semarang, Semarang 50275, Indonesia
Darma Wicaksana - Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Ahmad Arrayyan - Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Sakca Pratama - Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Mujib Al-Kamel - Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Hsing-Chung Chen - China Medical University, Taichung City 41354, Taiwan


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.2.2047

Abstract


Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.

Keywords


Diabetes Mellitus; continuous glucose monitoring; non-invasive; AI; CNN.

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References


G. S. Eisenbarth, “Type I diabetes mellitus. A chronic autoimmune disease,” N Engl J Med, vol. 314, no. 21, pp. 1360–8, May 1986, doi: 10.1056/NEJM198605223142106.

K. Gu, C. C. Cowie, and M. I. Harris, “Mortality in adults with and without diabetes in a National cohort of the U.S. Population, 1971-1993,” Diabetes Care, vol. 21, no. 7, pp. 1138–1145, 1998, doi: 10.2337/DIACARE.21.7.1138.

C. J. Sokolowski, J. A. Giovannitti, and S. G. Boynes, “Needle phobia: etiology, adverse consequences, and patient management,” Dent Clin North Am, vol. 54, no. 4, pp. 731–744, Oct. 2010, doi: 10.1016/J.CDEN.2010.06.012.

A. D. Association, “Introduction: Standards of Medical Care in Diabetes—2022,” Diabetes Care, vol. 45, no. Supplement_1, pp. S1–S2, Jan. 2022, doi: 10.2337/DC22-SINT.

M. B. Davidson, D. L. Schriger, A. L. Peters, and B. Lorber, “Relationship Between Fasting Plasma Glucose and Glycosylated Hemoglobin: Potential for False-Positive Diagnoses of Type 2 Diabetes Using New Diagnostic Criteria,” JAMA, vol. 281, no. 13, pp. 1203–1210, Apr. 1999, doi: 10.1001/JAMA.281.13.1203.

G. J. Eerdekens, S. Rex, and D. Mesotten, “Accuracy of Blood Glucose Measurement and Blood Glucose Targets,” J Diabetes Sci Technol, vol. 14, no. 3, pp. 553–559, May 2020, doi: 10.1177/1932296820905581.

L. Biagi, C. M. Ramkissoon, A. Facchinetti, Y. Leal, and J. Vehi, “Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor,” Sensors (Basel), vol. 17, no. 6, Jun. 2017, doi: 10.3390/S17061361.

O. Schubert-Olesen, J. Kröger, T. Siegmund, U. Thurm, and M. Halle, “Continuous Glucose Monitoring and Physical Activity,” Int J Environ Res Public Health, vol. 19, no. 19, Oct. 2022, doi: 10.3390/IJERPH191912296.

J. Zhou et al., “Reference values for continuous glucose monitoring in Chinese subjects,” Diabetes Care, vol. 32, no. 7, pp. 1188–1193, Jul. 2009, doi: 10.2337/DC09-0076/DC1.

Prayitno et al., “A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications,” Applied Sciences 2021, Vol. 11, Page 11191, vol. 11, no. 23, p. 11191, Nov. 2021, doi: 10.3390/APP112311191.

S. Siddiqi, F. Qureshi, S. Lindstaedt, and R. Kern, “Detecting Outliers in Non-IID Data: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 70333–70352, 2023, doi: 10.1109/ACCESS.2023.3294096.

“MDFD: STUDY OF DISTRIBUTED NON-IID SCENARIOS AND FRECHET DISTANCE-BASED EVALUATION | IEEE Resource Center.” Accessed: May 04, 2024. [Online]. Available: https://resourcecenter.ieee.org/conferences/icip-2023/spsicip23vid0623

K. T. Putra et al., “A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach,” IEEE Access, vol. 12, pp. 21437–21452, 2024, doi: 10.1109/ACCESS.2024.3358827.

K. T. Putra, I. Surahmat, A. N. Nazilah Chamim, M. Z. Ramadhan, D. Wicaksana, and R. A. Dhea Namyra Alissa, “Continuous Glucose Monitoring: A Non-Invasive Approach for Improved Daily Healthcare,” Proceedings - 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System: Responsible Technology for Sustainable Humanity, ICE3IS 2023, pp. 395–400, 2023, doi: 10.1109/ICE3IS59323.2023.10335328.

H. Rashtian, S. S. Torbaghan, S. Rahili, M. Snyder, and N. Aghaeepour, “Heart Rate and CGM Feature Representation Diabetes Detection from Heart Rate: Learning Joint Features of Heart Rate and Continuous Glucose Monitors Yields Better Representations,” IEEE Access, vol. 9, pp. 83234–83240, 2021, doi: 10.1109/ACCESS.2021.3085544.

A. Facchinetti, S. Favero, G. Sparacino, and C. Cobelli, “An online failure detection method of the glucose sensor-insulin pump system: improved overnight safety of type-1 diabetic subjects,” IEEE Trans Biomed Eng, vol. 60, no. 2, pp. 406–416, 2013, doi: 10.1109/TBME.2012.2227256.

B. Lobo, L. Farhy, M. Shafiei, and B. Kovatchev, “A Data-Driven Approach to Classifying Daily Continuous Glucose Monitoring (CGM) Time Series,” IEEE Trans Biomed Eng, vol. 69, no. 2, pp. 654–665, Feb. 2022, doi: 10.1109/TBME.2021.3103127.

C. Huang, Y. Xiao, and G. Xu, “Predicting Human Intention-Behavior through EEG Signal Analysis Using Multi-Scale CNN,” IEEE/ACM Trans Comput Biol Bioinform, vol. 18, no. 5, pp. 1722–1729, 2021, doi: 10.1109/TCBB.2020.3039834.

V. Senger and R. Tetzlaff, “New Signal Processing Methods for the Development of Seizure Warning Devices in Epilepsy,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 63, no. 5, pp. 609–616, May 2016, doi: 10.1109/TCSI.2016.2553278.

K. Zhao, H. Jiang, T. Yuan, C. Zhang, W. Jia, and Z. Wang, “A CNN based human bowel sound segment recognition algorithm with reduced computation complexity for wearable healthcare system,” Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2020-October, 2020, doi: 10.1109/ISCAS45731.2020.9180432/VIDEO.

S. Zhang, S. M. H. Bamakan, Q. Qu, and S. Li, “Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective,” IEEE Rev Biomed Eng, vol. 12, pp. 194–208, Aug. 2019, doi: 10.1109/RBME.2018.2864254.

M. Elhadary et al., “Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning,” Blood Rev, vol. 62, Nov. 2023, doi: 10.1016/J.BLRE.2023.101134.

O. Abdel-Hamid, A. R. Mohamed, H. Jiang, and G. Penn, “Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 4277–4280, 2012, doi: 10.1109/ICASSP.2012.6288864.

A. Ajit, K. Acharya, and A. Samanta, “A Review of Convolutional Neural Networks,” International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020, Feb. 2020, doi: 10.1109/IC-ETITE47903.2020.049.

N. Cahyana, S. Khomsah, and A. S. Aribowo, “Improving Imbalanced Dataset Classification Using Oversampling and Gradient Boosting,” Proceeding - 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, pp. 217–222, Oct. 2019, doi: 10.1109/ICSITECH46713.2019.8987499.

S. Maldonado, J. López, and C. Vairetti, “An alternative SMOTE oversampling strategy for high-dimensional datasets,” Appl Soft Comput, vol. 76, pp. 380–389, Mar. 2019, doi: 10.1016/J.ASOC.2018.12.024.

S. J. Basha, S. R. Madala, K. Vivek, E. S. Kumar, and T. Ammannamma, “A Review on Imbalanced Data Classification Techniques,” 2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022, 2022, doi: 10.1109/ICACTA54488.2022.9753392.

T. Potluri et al., “Secure Software Development in Google Colab,” 2023 IEEE World AI IoT Congress, AIIoT 2023, pp. 398–402, 2023, doi: 10.1109/AIIOT58121.2023.10174336.

N. L. Z. Msomi and B. A. Thango, “Development of Dissolved Gas Analysis-based Fault identification System using Machine Learning with Google Colab,” Proceedings of the 31st Southern African Universities Power Engineering Conference, SAUPEC 2023, 2023, doi: 10.1109/SAUPEC57889.2023.10057713.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2323, 1998, doi: 10.1109/5.726791.

J. An, H. Pedro Proença, B.-G. Kim, F. Rodríguez-Torres, J. F. Martínez-Trinidad, and J. A. Carrasco-Ochoa, “An Oversampling Method for Class Imbalance Problems on Large Datasets,” Applied Sciences 2022, Vol. 12, Page 3424, vol. 12, no. 7, p. 3424, Mar. 2022, doi: 10.3390/APP12073424.