Performance Improvement of Non Invasive Blood Glucose Measuring System With Near Infra Red Using Artificial Neural Networks
Abstract views: 62

Performance Improvement of Non Invasive Blood Glucose Measuring System With Near Infra Red Using Artificial Neural Networks

Rizaldi Ramdlani Pamungkas, Aji Gautama Putrada, Maman Abdurohman

Abstract

Measurement of body blood sugar levels is one of the important things to do to reduce the number of people with diabetes mellitus. Non-invasive measurement techniques become a blood sugar measurement technique that is more practical when compared to invasive techniques, but this technique has not shown too high levels of accuracy, specificity and sensitivity. For this reason, the non-invasive measurement model using NIR and ANN is proposed to improve the performance of non-invasive gauges. Non-invasive blood sugar measuring devices will be built using a nodemcu board with photodiaodes and NIR transmitters whose data is then processed using ANN models compared to invasive blood sugar data obtained from 40 data. 40 data obtained then used as raw data to build ANN models which 75% percent of it use as training data and 25% od it will be use as testing data to validate accuration of the model been built, the split of data doing randomly without any interference from programmer or model designer. All the data gathered are data collected from all volunteers which willingly to test their blood glucose using invasive glucose meter and non invasive glucose meter which been built. The invasive glucose meter used to gather raw data of blood glucose is SafeAccu-2 with 95% level of accuracy so the accuracy and error parameter calculated in this research are based on that 95% level accurcy of the invasive device.

Keywords

Diabetes, ANN, IoT, Glucose, Blood

Full Text:

PDF

References

[1] T. I. D. Federation, “IDF DIABETES ATLAS Eighth edition,” 2017.

[2] X. A. Guo, D. X.; Shang, Y. Z.; Peng, R.; Yong, S. S.; Wang, “Noninvasive Blood Glucose Measurement,” J. Biosci. Med., vol. 3, 2015.

[3] S. Dai, Juan; Ji, Zhong; Du, Yubao; Chen, “In vivo noninvasive blood glucose detection,” IOS Press, pp. 229–239, 2018.

[4] M. M. Buda, R. A.; Addi, “A Portable Non-Invasive Blood Glucose Monitoring Device,” in Conference on Biomedical Engineering and Sciences, 2014.

[5] V. A. Barbur, D. C. Montgomery, and E. A. Peck, “Introduction to Linear Regression Analysis.,” in The Statistician, 2006, vol. 43, no. 2, p. 339.

[6] M. A. Boatemaa and S. Doss, “Non-Invasive Glucose Estimation Based on Near Infrared Laser Diode Spectroscopy,” Asian J. Biomed. Pharm. Sci., vol. 7, no. 60, 2017.

[7] H. S. Kamasahayam, Swathi; K, “Non Invasive Estimation of Blood Glucose using Near Infra red Spectroscopy and Double Regression Analysis,” 2013.

[8] J. M. Derrick, Michele; Stulik, Dusan; Landry, Infrared Spectroscopy in Conservation Science (Tools for Conservation), 1st ed. Los Angeles, 1999.

[9] S. Menon, K. A. Unnikrishna; Gayathri, B.; K, “Non-invasive blood glucose monitoring using near infrared spectroscopy,” 2017.

[10] K. Gurney, An Introduction to Neural Networks, 1st ed. Boca Raton: CRC Press, 1997.

[11] G. I. Webb, Encyclopedia of Machine Learning and Data Mining Second Edition, 2nd ed. New York: Springer, 2017.

[12] ESP8266 Technical Reference. 2017.

[13] K. R. Tripalupi, Lulup Endah; Suwena, Statistika Dasar, 1st ed. Makassar: Graha ilmu.

[14] S. YL, Statistika Dasar, 1st ed. Yogyakarta: AndiPublisher, 2014.

[15] M. Parsian, Data Algorithms, 1st ed. Sebastopol: O’Reilly Media, Inc, 2015.

[16] A. F. M. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” Manila, 2018.

[17] F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi, “LEARNING ACTIVATION FUNCTIONS TO IMPROVEDEEP NEURAL NETWORKS,” in International Conference on Learning Representations, 2015.

[18] S. Bock, J. ; Goppold, and M. Weiß, “An improvement of the convergence proof of the ADAM-Optimizer,” in Clusterkonferenz, 2018.

[19] J. M. Chen, Chao; Twycross, Jamie; Garibaldi, “A new accuracy measure based on bounded relative error for time series forecasting,” PLoS One, 2017.

[20] R. R. Chai, T ; Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, pp. 1247–1250, 2014.

Refbacks

  • There are currently no refbacks.

Referencing Software:

Checked by:

Supervised by:

Statistic:

View My Stats


Creative Commons License Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control by http://kinetik.umm.ac.id is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.