Non-Invasive Hemoglobin Monitoring Device Using K-Nearest Neighbor and Artificial Neural Network Back Propagation Algorithms

• Autorzy: Munadi R. , Sussi S. , Fitriyanti N. , Ramadan D. N.

Identyfikatory

DOI

10.24425/ijet.2022.139842

• Języki publikacji: EN

Abstrakty

EN

The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Things-based HTTP protocol to achieve the high accuracy and the low end-to-end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm.

Słowa kluczowe

EN

invasive; non-invasive; k-nearest neighbor; artificial neural network; back propagation

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2022
• Tom: Vol. 68, No. 1
• Strony: 13--18
• Opis fizyczny: Bibliogr. 18 poz., schem., tab., wykr.

Twórcy

autor

Munadi R.

• Department Telecommunication Engineering, School of Electric Engineering, Telkom University. Indonesia

autor

Sussi S.

• Department Telecommunication Engineering, School of Electric Engineering, Telkom University. Indonesia

autor

Fitriyanti N.

• Department Physics Engineering, School of Electric Engineering, Telkom University. Indonesia

autor

Ramadan D. N.

• Diploma of Telecommunication Technology, School of Applied Science. Telkom University. Indonesia

Bibliografia

• [1] K. S. Pavithra, X. Anitha Mary, K. Rajasekaran, and R. Jegan, “Low Cost Non-Invasive Medical Device for Measuring Hemoglobin,” Proc. IEEE Int. Conf. Innov. Electr. Electron. Instrum. Media Technol. ICIEEIMT 2017, vol. 2017–January, pp. 197–200, 2017. https://doi.org/10.1109/ICIEEIMT.2017.8116834
• [2] S. Chugh and J. Kaur, “Non-invasive hemoglobin monitoring device,” 2015 Int. Conf. Control. Commun. Comput. India, ICCC 2015, no. November, pp. 380–383, 2016. https://doi.org/10.1109/ICCC.2015.7432925
• [3] P. Raikham, R. Kumar, R. K. Shah, M. Hazarika, and R. K. Sonkar, “Non-invasive blood components measurement using optical sensor system interface,” 2018 3rd Int. Conf. Microw. Photonics, ICMAP2018, vol. 2018-Janua, no. Icmap, pp. 1–2, 2018. https://doi.org/10.1109/ICMAP.2018.8354638
• [4] E. Kusumawati, N. Lusiana, I. Mustika, S. Hidayati, and E. N. Andyarini, “The Differences in the Result of Examination of Adolescent Hemoglobin Levels Using Sahli and Digital Methods (Easy Touch GCHb),” J. Heal. Sci. Prev., vol. 2, no. 2, pp. 95–99, 2018. https://doi.org/10.29080/jhsp.v2i2.128
• [5] R. A. Buda and M. M. Addi, “A portable non-invasive blood glucose monitoring device,” IECBES 2014, Conf. Proc. - 2014 IEEE Conf. Biomed. Eng. Sci. “Miri, Where Eng. Med. Biol. Humanit. Meet,” no. December, pp. 964–969, 2014. https://doi.org/10.1109/IECBES.2014.7047655
• [6] H. Ali, F. Bensaali, and F. Jaber, “Novel Approach to Non-Invasive Blood Glucose Monitoring Based on Transmittance and Refraction of Visible Laser Light,” IEEE Access, vol. 5, pp. 9163–9174, 2017. https://doi.org/10.1109/ACCESS.2017.2707384
• [7] Jiaxi Wan, Yuhua Zou, Ye Li, Jun Wang, “Reflective type blood oxygen saturation detection system based on MAX30100”, 2017 International Conference on Security, Pattern Analysis and Cybernetics (SPAC). https://doi.org/10.1109/SPAC.2017.8304350
• [8] K.S. Pavithra, X. Anitha Mary, K. Rajasekaran, R. Jegan, ”Low Cost Non-Invasive Medical Device For Measuring Hemoglobin”, Proceedings of IEEE International Conference on innovations in Electrical, Electronics, Instrumentation and Media Technology ICIEEIMT 17, 2017, pp. 197-200. https://doi.org/10.1109/ICIEEIMT.2017.8116834
• [9] Brinda Desai, Uttam Chaskar, ”Comparison of optical sensors for non-invasive hemoglobin measurement”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016. https://doi.org/10.1109/ICEEOT.2016.7755085
• [10] Raid Saleem Al-Baradie, Anandh Sam Chandra Bose, ”Portable Smart Non-Invasive Hemoglobin Measurement System”, 2013 10th International Multi-Conference on Systems, Signals & Devices (SSD) Hammamet, Tunisia, March 18-21, 2013. https://doi.org/10.1109/SSD.2013.6564149
• [11] U. Timma, G. Leena, E. Lewisa, D. McGrathb, J. Kraitlc and H. Ewald, “Non-Invasive Optical Real-time Measurement of Total Hemoglobin Content”, ELSEVIER, Proc. Eurosensors XXIV, September 5-8, 2010, page 488–491. https://doi.org/10.1016/j.proeng.2010.09.153
• [12] I. M. M. Yusoff, R. Yahya, W. R. W. Omar, and L. C. Ku, “Noninvasive cholesterol meter using Near Infrared sensor,” Proc. - 2015 Innov. Commer. Med. Electron. Technol. Conf. ICMET 2015, no. November, pp. 100–104, 2016. https://doi.org/10.1109/ICMETC.2015.7449581
• [13] N. A. Al-Sammarraie, “Classification and diagnosis using back propagation Artificial Neural Networks ( ANN ) algorithm,” 2018 Int. Conf. Smart Comput. Electron. Enterp., pp. 1–5, 2018. https://doi.org/10.1109/ICSCEE.2018.8538383
• [14] S. Imandoust and M. Bolandraftar, “Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background,” Int. J. Eng. Res. Appl., vol. 3, no. 5, pp. 605–610, 2013. www.ijera.com
• [15] P. Mulak and N. Talhar, “Analysis of Distance Measures Using K-Nearest Neighbor Algorithm on KDD Dataset,” Int. J. Sci. Res., vol. 4, no. 7, pp. 2319–7064, 2015.
• [16] S. B. Imandoust, Mohammad Bolandraftar,”Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background,” Int. Journal of Engineering Research and Applications, Vol. 3, Issue 5, Sep-Oct 2013, pp. 605-610. www.ijera.com
• [17] Kalyani Adawadkar. “Python Programming-Applications and Future”, Scientific Journal of Impact Factor (SJIF), Special Issue SIEICON-2017, April - 2017.
• [18] David Nettikadan and Subodh Raj M. S., “Smart Community Monitoring System using ThingSpeak IoT Platform.”, International Journal of Applied Engineering Research, Volume 13, Number 17 (2018) pp. 13402-13408.