Skip to main content
SpringerLink
Log in
Book cover

World Conference on Information Systems and Technologies

WorldCIST 2021: Trends and Applications in Information Systems and Technologies pp 544–554Cite as

Ensemble Regression for Blood Glucose Prediction

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1365))

Abstract

Background: Predicting blood glucose present commonly many challenges when the designed models are tested under different contexts. Ensemble methods are a set of learning algorithms that have been successfully used in many medical fields to improve the prediction accuracy. This paper aims to review the typology of ensembles used in literature to predict blood glucose.

Methods: 32 papers published between 2000 and 2020 in 6 digital libraries were selected and reviewed with regard to: years and publication sources, integrated factors and data sources used to collect the data and types of ensembles.

Results: This review results found that this research topic is still recent but is gaining a growing interest in the last years. Ensemble models used often blood glucose, insulin, diet and exercise as input to predict blood glucose. Moreover, both homogeneous and heterogeneous ensembles have been investigated.

Conclusions: An increasing interest have been devoted to blood glucose prediction using ensemble methods during the last decade. Several gaps regarding the design of the reviewed ensembles and the data collection process have been identified and recommendations have been formulated in this direction.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Williams, G., Pickup, J.C.: Handbook of Diabetes. Wiley-Blackwell, Malden, Mass (2004)

    Google Scholar 

  2. Leon, B.M., Maddox, T.M.: Diabetes and cardiovascular disease: epidemiology, biological mechanisms, treatment recommendations and future research. World J. Diab. 6, 1246–1258 (2015)

    Article  Google Scholar 

  3. Chen, C., Wang, C., Hu, C., Han, Y., Zhao, L., Zhu, X., Xiao, L., Sun, L.: Normoalbuminuric diabetic kidney disease. Front Med. 11, 310–318 (2017)

    Article  Google Scholar 

  4. Khadilkar, K.S., Bandgar, T., Shivane, V., Lila, A., Shah, N.: Current concepts in blood glucose monitoring. Indian J. Endocrinol. Metab. 17, 643 (2013)

    Article  Google Scholar 

  5. Abraham, S.B., Arunachalam, S., Zhong, A., Agrawal, P., Cohen, O., McMahon, C.M.: Improved real-world glycemic control with continuous glucose monitoring system predictive alerts. J. Diabetes Sci. Technol. 15(1), 91–97 (2019). 1932296819859334

    Google Scholar 

  6. Teng, X., Gong, Y.: Research on application of machine learning in data mining. IOP Conf. Ser.: Mater. Sci. Eng. 392, 062202 (2018)

    Google Scholar 

  7. Woldaregay, A.Z., Årsand, E., Walderhaug, S., Albers, D., Mamykina, L., Botsis, T., Hartvigsen, G.: Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes. Artif. Intell. Med. 98, 109–134 (2019)

    Article  Google Scholar 

  8. Seni, G., Elder, J.F.: Ensemble methods in data mining: improving accuracy through combining predictions. Synth. Lectures Data Mining Knowl. Disc. 2, 1–26 (2010)

    Article  Google Scholar 

  9. Hosni, M., Carrillo de Gea, J.M., Idri, A., El Bajta, M., Fernández Alemán, J.L., García-Mateos, G., Abnane, I.: A systematic mapping study for ensemble classification methods in cardiovascular disease. Artif. Intell. Rev. 1–35 (2020)

    Google Scholar 

  10. Hosni, M., Abnane, I., Idri, A., Carrillo de Gea, J.M., Fernández Alemán, J.L.: Reviewing ensemble classification methods in breast cancer. Comput. Methods Programs Biomed. 177, 89–112 (2019)

    Google Scholar 

  11. Fernández-Alemán, J.L., Carrillo-de-Gea, J.M., Hosni, M., Idri, A., García-Mateos, G.: Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2019)

    Google Scholar 

  12. El Idrissi, T., Idri, A., Bakkoury, Z.: Systematic map and review of predictive techniques in diabetes self-management. Int. J. Inf. Manage. 46, 263–277 (2019)

    Google Scholar 

  13. El Idrissi, T., Idri, A., Bakkoury, Z.: Data mining techniques in diabetes self-management: a systematic map. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) Trends and Advances in Information Systems and Technologies. Springe, Cham (2018)

    Google Scholar 

  14. Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., Chouvarda, I.: Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15, 104–116 (2017)

    Article  Google Scholar 

  15. Oviedo, S., Vehí, J., Calm, R., Armengol, J.: A review of personalized blood glucose prediction strategies for T1DM patients. Int. J. Numer. Methods Biomed. Eng. 33, e2833 (2017)

    Google Scholar 

  16. Kitchenham, B.A., Budgen, D., Pearl Brereton, O.: Using mapping studies as the basis for further research – a participant-observer case study. Inf. Softw. Technol. 53, 638–651 (2011)

    Article  Google Scholar 

  17. Petersen, K., Vakkalanka, S., Kuzniarz, L.: Guidelines for conducting systematic mapping studies in software engineering: an update. Inf. Softw. Technol. 64, 1–8 (2015)

    Article  Google Scholar 

  18. Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, Boca Raton (2012)

    Book  Google Scholar 

  19. Fonda, S.J., Graham, C., Munakata, J., Powers, J.M., Price, D., Vigersky, R.A.: The cost-effectiveness of real-time continuous glucose monitoring (RT-CGM) in type 2 diabetes. J Diabetes Sci. Technol. 10, 898–904 (2016)

    Article  Google Scholar 

  20. Freckmann, G., Kamecke, U., Waldenmaier, D., Haug, C., Ziegler, R.: Accuracy of bolus and basal rate delivery of different insulin pump systems. Diabetes Technol. Ther. 21, 201–208 (2019)

    Article  Google Scholar 

  21. Kerkenbush, N.L.: A comparison of self-documentation in diabetics: electronic versus paper diaries. In: AMIA Annual Symposium Proceedings 2003, vol. 887 (2003)

    Google Scholar 

  22. Man, C.D., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C.: The UVA/PADOVA type 1 diabetes simulator. J. Diabetes Sci. Technol. 8, 26–34 (2014)

    Article  Google Scholar 

  23. Rodríguez-Rodríguez, I., Rodríguez, J.V., Chatzigiannakis, I., Zamora Izquierdo, M.Á.: On the possibility of predicting glycaemia ‘On the Fly’ with constrained IoT devices in type 1 diabetes mellitus patients. Sensors 19, 4538 (2019)

    Google Scholar 

  24. Liu, J., Wang, L., Zhang, L., Zhang, Z., Zhang, S.: Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach. Library Hi Tech. ahead-of-print (2020)

    Google Scholar 

  25. Saiti, K., Macaš, M., Lhotská, L., Štechová, K., Pithová, P.: Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus. Comput. Methods Programs Biomed. 196, 105628 (2020)

    Article  Google Scholar 

  26. Midroni, C., Leimbigler, P.J., Baruah, G., Kolla, M., Whitehead, A.J., Fossat, Y.: Predicting glycemia in type 1 diabetes patients: experiments with XGBoost. Presented at the 3rd International workshop on knowledge discovery in healthcare data (2018)

    Google Scholar 

  27. Wang, Y., Wang, T.: Application of improved LightGBM model in blood glucose prediction. Appl. Sci. 10, 3227 (2020)

    Article  Google Scholar 

  28. Alfian, G., Syafrudin, M., Rhee, J., Anshari, M., Mustakim, M., Fahrurrozi, I.: Blood glucose prediction model for type 1 diabetes based on extreme gradient boosting. IOP Conf. Ser.: Mater. Sci. Eng. 803, 012012 (2020)

    Google Scholar 

  29. Xao, W., Shao, F., Ji, J., Sun, R., Xing, C.: Fasting blood glucose change prediction model based on medical examination data and data mining techniques. In: 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). IEEE, Chengdu (2015)

    Google Scholar 

  30. Georga, E.I., Protopappas, V.C., Polyzos, D., Fotiadis, D.I.: A predictive model of subcutaneous glucose concentration in type 1 diabetes based on Random Forests. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, San Diego (2012)

    Google Scholar 

  31. Hidalgo, J.I., Colmenar, J.M., Kronberger, G., Winkler, S.M., Garnica, O., Lanchares, J.: Data based prediction of blood glucose concentrations using evolutionary methods. J. Med. Syst. 41, 142 (2017)

    Article  Google Scholar 

  32. Hidalgo, J.I., Botella, M., Velasco, J.M., Garnica, O., Cervigón, C., Martínez, R., Aramendi, A., Maqueda, E., Lanchares, J.: Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and bagging. Appl. Soft Comput. 88, 105923 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SPM Research Team, ENSIAS, Mohammed V University in Rabat, BP 713, Rabat, Agdal, Morocco

    Mohamed Zaim Wadghiri, Ali Idri & Touria El Idrissi

  2. MSDA, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Ali Idri

Authors
  1. Mohamed Zaim Wadghiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Idri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Touria El Idrissi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Idri .

Editor information

Editors and Affiliations

  1. ISEG, University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Department of Information Sciences, University of Sheffield, Lisbon, Portugal

    Ana Maria Ramalho Correia

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wadghiri, M.Z., Idri, A., Idrissi, T.E. (2021). Ensemble Regression for Blood Glucose Prediction. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_52

Download citation

Publish with us

Policies and ethics

Search

Navigation