CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

LSTM Variants in Blood Glucose Prediction for Diabetic Patients

by Rita Ganguly
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 39
Year of Publication: 2022
Authors: Rita Ganguly
10.5120/ijca2022922489

Rita Ganguly . LSTM Variants in Blood Glucose Prediction for Diabetic Patients. International Journal of Computer Applications. 184, 39 ( Dec 2022), 8-12. DOI=10.5120/ijca2022922489

@article{ 10.5120/ijca2022922489,
author = { Rita Ganguly },
title = { LSTM Variants in Blood Glucose Prediction for Diabetic Patients },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2022 },
volume = { 184 },
number = { 39 },
month = { Dec },
year = { 2022 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number39/32569-2022922489/ },
doi = { 10.5120/ijca2022922489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:32.856823+05:30
%A Rita Ganguly
%T LSTM Variants in Blood Glucose Prediction for Diabetic Patients
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 39
%P 8-12
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The prediction and management of blood glucose level is indispensable for precautionary of diabetic management. The precious tool like Continuous Glucose Monitoring (CGM) system is used to minutely monitoring and refluxing the blood current glucose level of patients. The result whatever generated by CGM can be very useful prediction of future blood glucose levels with the help of machine learning models. In this research introducing a CGM based approaches on recurrent deep learning which will forecast the future blood glucose levels from the obtained data. In case of deep learning LSTM (Long Short Term Memory) and derived LSTM are used in various complex fields with complex time series data. In this study a survey report is established to determine blood glucose with LSTM based different variants to generate more accuracy in results. The goal of this survey is to lower the differences between predicted CGM values and the finger stick blood glucose readings. The output might indicate that this survey approach is realizable for more appreciable forecasting of BG that enhance and enrich the diabetes management.

References
  1. American Diabetes A (2014) Diagnosis and classifcation of diabetes mellitus. Diabetes Care 37(Suppl 1):S81-90. https://doi. org/10.2337/dc14-S081 (Epub 2013/12/21, PubMed PMID: 24357215)
  2. Kirchsteiger H, Jørgensen JB, Renard E, Del Re L (2015) Prediction Methods for Blood Glucose Concentration: Design, Use and Evaluation. Springer, Berlin
  3. Klonof DC (2005) Continuous glucose monitoring roadmap for 21st century diabetes therapy. Diabetes Care 28(5):1231–1239
  4. Deiss D, Bolinder J, Riveline J-P, Battelino T, Bosi E, TubianaRuf N et al (2006) Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring. Diabetes Care 29(12):2730–2732
  5. The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2008) Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008(359):1464–1476
  6. Ozogur HN, Ozogur G, Orman Z (2020) Blood glucose level prediction for diabetes based on modifed fuzzy time series and particle swarm optimization. Comput Intell-US. https://doi.org/ 10.1111/coin.12396 (PubMedPMID:WOS:000566826000001)
  7. Xia Y, Rashid M, Feng J, Hobbs N, Cinar A (2020) Online glucose prediction using computationally efcient sparse kernel fltering algorithms in type-1 diabetes. IEEE Trans Control Syst Technol 28(1):3–15
  8. Woldaregay AZ, Arsand E, Walderhaug S, Albers D, Mamykina L, Botsis T et al (2019) Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes. Artif Intell Med 98:109–134. https://doi.org/10.1016/j. artmed.2019.07.007 (PubMedPMID:WOS:000488323400010)
  9. Yu X, Turksoy K, Rashid M, Feng JY, Hobbs N, Hajizadeh I et al (2018) Model-fusion-based online glucose concentration predictions in people with type 1 diabetes. Control Eng Pract 71:129– 141. https://doi.org/10.1016/j.conengprac.2017.10.013 (PubMe dPMID:WOS:000424175400013)
  10. Oviedo S, Vehi J, Calm R, Armengol J (2017) A review of personalized blood glucose prediction strategies for T1DM patients. Int J Numer Method Biomed Eng. https://doi.org/10.1002/cnm. 2833 (Epub 2016/10/30, PubMed PMID: 27644067)
  11. Georga EI, Protopappas VC, Polyzos D, Fotiadis DI (2015) Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models. Med Biol Eng Comput 53(12):1305–1318. https://doi.org/10.1007/ s11517-015-1263-1 (PubMedPMID:WOS:000365753000006)
  12. Munoz-Organero M (2020) Deep physiological model for blood glucose prediction in T1DM patients. Sensors. https://doi.org/10. 3390/s20143896 (PubMed PMID: WOS:000557994000001)
  13. Montaser E, Diez JL, Rossetti P, Rashid M, Cinar A, Bondia J (2020) Seasonal Local Models for Glucose Prediction in Type 1 Diabetes. IEEE J Biomed Health Inform 24(7):2064–2072. https://doi.org/10.1109/Jbhi.2019.2956704 (PubMedPMID: WOS:000545429400022)
  14. Reifman J, Rajaraman S, Gribok A, Ward WK (2007) Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol 1(4):478–486
  15. Cherkassky V, Mulier FM (2007) Learning from data: concepts, theory, and methods. Wiley, New YOrk
  16. Araghinejad S (2013) Data-driven modeling: using MATLAB® in water resources and environmental engineering. Springer Science & Business Media, Berlin
  17. Diabetes Research in Children Network Study Group (2005) Impact of exercise on overnight glycemic control in children with type 1 diabetes mellitus. J Pediatr 147(4):528–534
  18. Nomura M, Fujimoto K, Higashino A, Denzumi M, Miyagawa M, Miyajima H et al (2000) Stress and coping behavior in patients with diabetes mellitus. Acta Diabetol 37(2):61–64
  19. Brazeau A-S, Rabasa-Lhoret R, Strychar I, Mircescu H (2008) Barriers to physical activity among patients with type 1 diabetes. Diabetes Care 31(11):2108–2109
  20. Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C (2007) Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng 54(5):931–937. https://doi.org/10. 1109/TBME.2006.889774 (Epub 2007/05/24, PubMed PMID: 17518291)
  21. Reymann MP, Dorschky E, Groh BH, Martindale C, Blank P, Eskofer BM (2016) Blood glucose level prediction based on support vector regression using mobile platforms. Conf Proc IEEE Eng Med Biol Soc 2016:2990–2993. https://doi.org/10.1109/ EMBC.2016.7591358 (Epub 2017/03/09, PubMed PMID: 28268941)
  22. Pérez-Gandía C, Facchinetti A, Sparacino G, Cobelli C, Gómez E, Rigla M et al (2010) Artifcial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther 12(1):81–88
  23. Mirshekarian S, Bunescu R, Marling C, Schwartz F (2017) Using LSTMs to learn physiological models of blood glucose behavior. Conf Proc IEEE Eng Med Biol Soc 2017:2887–2891. https://doi. org/10.1109/EMBC.2017.8037460 (Epub 2017/10/25, PubMed PMID: 29060501)
  24. Li K, Daniels J, Liu C, Herrero P, Georgiou P (2020) Convolutional recurrent neural networks for glucose prediction. IEEE J Biomed Health Inform 24(2):603–613. https://doi.org/10. 1109/JBHI.2019.2908488 (Epub 2019/04/05, PubMed PMID: 30946685)
  25. Aiello EM, Lisanti G, Magni L, Musci M, Tofanin C (2020) Therapy-driven deep glucose forecasting. Eng Appl Artif Intel. https://doi.org/10.1016/j.engappai.2019.103255
  26. Mosquera-Lopez C, Dodier R, Tyler N, Resalat N, Jacobs P (2019) Leveraging a big dataset to develop a recurrent neural network to predict adverse glycemic events in type 1 diabetes. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2019.29117 01 (Epub 2019/04/19, PubMed PMID: 30998484) Complex & Intelligent Systems 1 3
  27. Eberle C, Ament C (2012) Real-time state estimation and longterm model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels. J Diabetes Sci Technol 6(5):1148–1158. https://doi.org/10.1177/193229681200600520 (Epub 2012/10/16, PubMed PMID: 23063042; PubMed Central PMCID: PMCPMC3570850)
  28. Balakrishnan NP, Rangaiah GP, Samavedham L (2012) Personalized blood glucose models for exercise, meal and insulin interventions in type 1 diabetic children. Conf Proc IEEE Eng Med Biol Soc 2012:1250–1253. https://doi.org/10.1109/EMBC.2012.63461 64 (Epub 2013/02/01, PubMed PMID: 23366125)
  29. Capel I, Rigla M, Garcia-Saez G, Rodriguez-Herrero A, Pons B, Subias D et al (2014) Artifcial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes. Diabetes Technol Ther 16(3):172– 179. https://doi.org/10.1089/dia.2013.0229 (Epub 2013/10/25, PubMed PMID: 24152323; PubMed Central PMCID: PMCPMC3934437)
  30. Luo S, Zhao C (2019) Transfer and incremental learning method for blood glucose prediction of new subjects with type 1 diabetes. In: 2019 12th Asian control conference (ASCC); Kitakyushu-shi, Japan, pp 73–78
  31. S. Hochreiter, J. Schmidhuber: Long short-term memory. Neural computation, (1997), 1735–1780.
  32. Ashour, A. S., El-Attar, A., Dey, N., Abd El-Kader, H., & Abd El-Naby, M. M. (2020). Long short term memory based patient-dependent model for FOG detection in Parkinson's disease. Pattern recognition letters, 131, 23-29.
  33. Wang, Y., Wu, Q., Dey, N., Fong, S., & Ashour, A. S. (2020). Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation. Biocybernetics and Biomedical Engineering, 40(3), 987-1001.
  34. Lan, K., Wang, D. T., Fong, S., Liu, L. S., Wong, K. K., & Dey, N. (2018). A survey of data mining and deep learning in bioinformatics. Journal of medical systems, 42(8), 1-20.
  35. S. Park, S. Min, H.-S. Choi, and S. Yoon, “Deep Recurrent Neural Network-Based Identification of Precursor microRNAs,” Nips, no. Nips, 2017.
  36. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.
  37. G. Trigeorgis, F. Ringeval, R. Brueckner, E. Marchi, M. A. Nicolaou, B. Schuller, and S. Zafeiriou, “Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,” in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016, pp. 5200–5204.
  38. Sainath TN, Vinyals O, Senior A, Sak H. Convolutional, long short-term memory, fully connected deep neural networks. In2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 Apr 19 (pp. 4580-4584). IEEE.
  39. Wu Y, Yuan M, Dong S, Lin L, Liu Y. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing. 2018 Jan 31;275:167-79.
  40. P. Su, X. Ding, Y. Zhang, F. Miao, and N. Zhao, “Learning to Predict Blood Pressure with Deep Bidirectional LSTM Network,” pp. 1–19, 2017.
  41. A. Graves, “Generating sequences with recurrent neural networks,” arXiv preprint arXiv:1308.0850, 2013.
  42. Li, Z., Wang, D., Dey, N., Ashour, A. S., Sherratt, R. S., & Shi, F. (2019). Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model. Biocybernetics and Biomedical Engineering, 39(3), 742-752.
  43. Elhayatmy, G., Dey, N., & Ashour, A. S. (2018). Internet of Things based wireless body area network in healthcare. In Internet of things and big data analytics toward next-generation intelligence (pp. 3-20). Springer, Cham.
  44. Wang,T and Li.W(2020).Blood Glucose Forecasting Using LSTM Variants Under the Context of Open Source Artiicial Pancreas System. Proceedings of the 53rd Hawaii International Conference on System Science,pp-3257-3263.
Index Terms

Computer Science
Information Sciences

Keywords

LSTM CGM Deep Learning Blood Glucose RNN

Powered by PhDFocusTM