Application of Artificial Neural Network and Gray Level Co-occurrence Matrix to detect blood glucose levels through the skin of the hands.
Abstract
Increased glucose in the blood can cause a buildup so that it cannot be absorbed by all of the body's cells, this problem can cause various disorders in the body's organs. To avoid problems, it is necessary to check the blood glucose level regularly. Monitoring blood sugar levels is currently still using invasive techniques that are painful, non-invasive monitoring is needed. This study develops a non-invasive method to predict blood glucose through image processing. For investigation, several invasive images and glucose levels were taken. Types of samples based on age classification, 20-60 years. For accuracy and simple analysis, 37 images of participants as volunteers, samples were evaluated and investigated under the gray level co-occurrence matrix (GLCM). In this study, an artificial neural network (ANN) was used for all training and hand texture testing to detect glucose levels. The performance of this model is evaluated using Root Mean Square Error (RMSE) and correlation coefficient (r). Clarke Error Grid Analysis (EGA) variance was used in this investigation to determine the accuracy of the method. The results showed that the RMSE was close to the standard value, the regression coefficient was 0.95, and the Clarke EGA analysis: 81.08 % was in the A zone. So that the blood glucose prediction model using the GLCM-ANN method is feasible to apply.
Keywords
Full Text:
PDFReferences
W. Zheng et al., “Highly-sensitive and reflective glucose sensor based on optical fiber surface plasmon resonance,†Microchem. J., vol. 157, no. February, p. 105010, 2020, doi: 10.1016/j.microc.2020.105010.
X. Dong et al., “Influence of blood glucose level on the prognosis of patients with diabetes mellitus complicated with ischemic stroke,†J. Res. Med. Sci. |, no. 65, pp. 1–7, 2018, doi: 10.4103/1735-1995.223951.
K. S. P and S. Am, “A study on the glycemic , lipid and blood pressure control among the type 2 diabetes patients of north Kerala , India,†Indian Heart J., vol. 70, no. 4, pp. 482–485, 2018, doi: 10.1016/j.ihj.2017.10.007.
G. Amar and A. Kuppusamy, “In-vitro Study of Trachyspermum ammi extract against Diabetes Mellitus,†TRENDS Sci., vol. 19, no. 16, 2022.
C. Beehan-quirk et al., “Investigating the effects of fatigue on blood glucose levels – implications for diabetes,†Transl. Metab. Syndr. Res., 2020, doi: 10.1016/j.tmsr.2020.03.001.
K. Ogurtsova et al., “IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040,†Diabetes Res. Clin. Pract., vol. 128, pp. 40–50, 2017, doi: 10.1016/j.diabres.2017.03.024.
Y. fu Sun, Y. Song, C. sheng Liu, and J. li Geng, “Correlation between the glucose level and the development of acute pancreatitis,†Saudi J. Biol. Sci., vol. 26, no. 2, pp. 427–430, 2019, doi: 10.1016/j.sjbs.2018.11.012.
S. R. Chinnadayyala, J. Park, A. T. Satti, D. Kim, and S. Cho, “Minimally invasive and continuous glucose monitoring sensor based on non-enzymatic porous platinum black-coated gold microneedles,†Electrochim. Acta, vol. 369, p. 137691, Feb. 2021, doi: 10.1016/j.electacta.2020.137691.
S. Ghosal, A. Kumar, V. Udutalapally, and D. Das, “glucam: Smartphone based blood glucose monitoring and diabetic sensing,†IEEE Sens. J., vol. 21, no. 21, pp. 24869–24878, 2021.
N. Duc, T. Van Nguyen, A. Duc, and H. Vinh, “ORIGINAL ARTICLE A label-free colorimetric sensor based on silver nanoparticles directed to hydrogen peroxide and glucose,†Arab. J. Chem., vol. 11, no. 7, pp. 1134–1143, 2018, doi: 10.1016/j.arabjc.2017.12.035.
F. Rui, G. Zhanxiao, L. Ang, C. Yao, W. Chenyang, and Z. Ning, “Sensors and Actuators : B . Chemical Noninvasive blood glucose monitor via multi-sensor fusion and its clinical evaluation,†Sensors Actuators B. Chem., vol. 332, no. September 2020, p. 129445, 2021, doi: 10.1016/j.snb.2021.129445.
V. P. Rachim and W. Y. Chung, “Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring,†Sensors Actuators, B Chem., vol. 286, pp. 173–180, 2019, doi: 10.1016/j.snb.2019.01.121.
H. Zhang, Z. Chen, J. Dai, W. Zhang, Y. Jiang, and A. Zhou, “A low-cost mobile platform for whole blood glucose monitoring using colorimetric method,†Microchem. J., vol. 162, no. October 2020, p. 105814, 2021, doi: 10.1016/j.microc.2020.105814.
N. Eldeen, K. Mohamed, and L. Seyedali, “A comprehensive survey of recent trends in deep learning for digital images augmentation,†Artif. Intell. Rev., vol. 55, no. 3, pp. 2351–2377, 2022, doi: 10.1007/s10462-021-10066-4.
S. Agreste and G. Andaloro, “A new approach to pre-processing digital image for wavelet-based watermark,†J. Comput. Appl. Math., vol. 221, pp. 274–283, 2008, doi: 10.1016/j.cam.2007.10.057.
M. A. Jardine, J. A. Miller, and M. Becker, “Coupled X-ray computed tomography and grey level co-occurrence matrices as a method for quantification of mineralogy and texture in 3D,†Comput. Geosci., vol. 111, pp. 105–117, 2018, doi: 10.1016/j.cageo.2017.11.005.
I. Pantic, D. Dimitrijevic, D. Nesic, and D. Petrovic, “Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture,†J. Theor. Biol., vol. 406, pp. 124–128, 2016, doi: 10.1016/j.jtbi.2016.07.018.
P. K. Bhagat, P. Choudhary, and K. M. Singh, A comparative study for brain tumor detection in MRI images using texture features. Elsevier Inc., 2019.
J. Waiwattana, C. Asawatangtrakuldee, P. Saksirimontri, V. Wachirapusitanand, and N. Pitakkultorn, “Application of Machine Learning Algorithms for Searching BSM Higgs Bosons Decaying to a Pair of Bottom Quarks,†TRENDS Sci., vol. 19, no. 19, pp. 1–15, 2022, [Online]. Available:https://tis.wu.ac.th/index.php/tis/article/view/5373/380
D. A. Anggoro and D. Novitaningrum, “Machine ( Svm ) And Artificial Neural Network ( ANN ),†ICIC Express Lett., vol. 15, no. 1, pp. 9–18, 2021, doi: 10.24507/icicel.15.01.9.
P. Agarwal and R. K. Srivastava, “INTRUSION DETECTION WITH NEIGHBOURHOOD COMPONENT ANALYSIS,†ICIC Express Lett., vol. 15, no. 12, pp. 1–8, 2021, doi: 10.24507/icicel.15.12.XXX.
P. R. Vlachas, J. Pathak, B. R. Hunt, T. P. Sapsis, M. Girvan, and E. Ott, “Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics,†Neural Networks, vol. 126, pp. 191–217, 2020, doi: 10.1016/j.neunet.2020.02.016
M. S. A.-H. Noor Sabah, Ekhlas Hamed, “Optimal Sliding Mode Controller Design Based On Whale Optimization Algorithm For Lower Limb Rehabilitation Robot,†Appl. Comput. Sci., vol. 17, no. 3, pp. 47–59, 2021, doi: 10.23743/acs-2021-20.
T. Lu, X. Lü, H. Salonen, and Q. Zhang, “Novel hybrid modeling approach for utilizing simple linear regression models to solve multi-input nonlinear problems of indoor humidity modeling,†Build. Environ., vol. 213, p. 108856, 2022, doi: https://doi.org/10.1016/j.buildenv.2022.108856
H. Mondal and S. Mondal, “Clarke Error Grid Analysis on Graph Paper and Microsoft Excel,†J. Diabetes Sci. Technol., vol. 14, no. 2, p. 499, 2019, doi: 10.1177/1932296819890875.
K. Chowdhury, A. Srivastava, N. Sharma, and S. Sharma, “Error Grid Analysis of Reference and Predicted Blood Glucose Level Values as Obtained from the Normal and Prediabetic Human Volunteers,†Am. J. Biomed. Eng., vol. 5, no. 1, pp. 6–14, 2015, doi: 10.5923/j.ajbe.20150501.02.
C. Hernandez-Matas, A. A. Argyros, and X. Zabulis, Retinal image preprocessing, enhancement, and registration. Elsevier Ltd., 2019
S. Gao, “Gray level co-occurrence matrix and extreme learning machine for Alzheimer’s disease diagnosis,†Int. J. Cogn. Comput. Eng., vol. 2, no. July, pp. 116–129, 2021, doi: 10.1016/j.ijcce.2021.08.002
D. Koundal and B. Sharma, Advanced neutrosophic set-based ultrasound image analysis. Elsevier Inc., 2019.
S. Punitha, F. Al-turjman, and T. Stephan, “An automated breast cancer diagnosis using feature selection and parameter optimization in ANN ☆,†Comput. Electr. Eng., vol. 90, no. July 2020, p. 106958, 2021, doi: 10.1016/j.compeleceng.2020.106958
H. T. Ho, W. K. Y. Yeung, and B. W. Y. Young, “Evaluation of ‘point of care’ devices in the measurement of low blood glucose in neonatal practice,†Arch. Dis. Child. Fetal Neonatal Ed., vol. 89, no. 4, pp. 356–360, 2004, doi: 10.1136/adc.2003.033548.
DOI: http://dx.doi.org/10.31963/elekterika.v6i2.3756
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Usman Umar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Publisher by:
Politeknik Negeri Ujung Pandang
Jurusan Teknik Elektro
Kampus 2 Moncongloe
Jalan Tamalanrea Raya (BTP)
Makassar 90245
E-mail: jurnal-elektrika@poliupg.ac.id
Jurnal Teknologi Elekterika Indexed by:
Member of :
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
