Data Mining Techniques in Diabetes Self-management: A Systematic Map

  • Touria El Idrissi
  • Ali Idri
  • Zohra Bakkoury
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


Data mining techniques (DMT) provide powerful tools to extract knowledge from data helping in decision making. Medicine, like many other fields, is using DM in diabetes, cardiology, cancer and other diseases. In this paper, we investigate the use of DMT in diabetes, in particular in diabetes self-management (DSM). The purpose is to conduct a systematic mapping study to review primary studies investigating DMT in DSM. This mapping study aims to summarize and analyze knowledge on: (1) years and sources of DSM publications, (2) type of diabetes that took most attention, (3) DM tasks and techniques most frequently used, and (4) the considered functionalities. A total of 57 papers published between 2000 and April 2017 were selected and analyzed regarding four research questions. The study shows that prediction was largely the most used DM task and Neural Networks were the most frequently used technique. Moreover, T1DM is largely the type of diabetes that is most concerned by the studies so as the Prediction of blood glucose.


Systematic mapping study Data mining Diabetes Self-management 



This research is part of the project “mPHR in Morocco” financed by the Ministry of High education and Scientific research in Morocco 2015-2017.


  1. 1.
    Billous, R., Donnally, R.: Handbook of Diabetes. Blackwell, Hoboken (2010)CrossRefGoogle Scholar
  2. 2.
    Sparacino, G., et al.: Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans. Biomed. Eng. 54(5), 931–937 (2007)CrossRefGoogle Scholar
  3. 3.
    Cescon, M., Renard, E.: Adaptive subspace-based prediction of T1DM glycemia. In: 50th IEEE Conference on Decision and Control and European Control Conference, pp. 5164–5169 (2011)Google Scholar
  4. 4.
    Alanis, A.Y., et al.: Neural model of blood glucose level for Type 1 Diabetes Mellitus patients. In: The International Joint Conference on Neural Networks, San Jose, CA, pp. 2018–2023 (2011)Google Scholar
  5. 5.
    Esfandiari, N., et al.: Knowledge discovery in medicine: current issue and future trend. Expert Syst. Appl. 41(9), 4434–4463 (2014)CrossRefGoogle Scholar
  6. 6.
    Marinov, M., Mosa, A.S.M., Yoo, I., Boren, S.A.: Data-mining technologies for diabetes: a systematic review. J. Diab. Sci. Technol. 5(6), 1549–1556 (2011)CrossRefGoogle Scholar
  7. 7.
    Kadi, I., Idri, A., Fernandez-Aleman, J.-L.: Knowledge discovery in cardiology: a systematic literature review. Int. J. Med. Inf. 97, 12–32 (2017)CrossRefGoogle Scholar
  8. 8.
    Baghdadi, G., Nasrabadi, A.M.: Controlling blood glucose levels in diabetics by neural network predictor. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, pp. 3216–3219 (2007)Google Scholar
  9. 9.
    Allam, F., et al.: Prediction of subcutaneous glucose concentration for type-1 diabetic patients using a feed forward neural network. In: The International Conference on Computer Engineering & Systems, Cairo, pp. 129–133 (2011)Google Scholar
  10. 10.
    Zarkogianni, K., et al.: An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Trans. Biomed. Eng. 58(9), 2467–2477 (2011)CrossRefGoogle Scholar
  11. 11.
    Mathiyazhagan, N., Schechter, H.B.: Soft computing approach for predictive blood glucose management using a fuzzy neural network. In: IEEE Conference on Norbert Wiener in the 21st Century (21CW), Boston, MA, pp. 1–3 (2014)Google Scholar
  12. 12.
    Wang, Q., et al.: Developing personalized empirical models for Type-I Diabetes: an extended Kalman filter approach. In: American Control Conference, pp. 2923–2928 (2013)Google Scholar
  13. 13.
    Lynch, S.M., Bequette, B.W.: Model predictive control of blood glucose in Type I Diabetics using subcutaneous glucose measurements. In: Proceedings of the 2002 American Control Conference, Anchorage, AK, USA, vol. 5, pp. 4039–4043 (2002)Google Scholar
  14. 14.
    Bunescu, R., et al.: Blood glucose level prediction using physiological models and support vector regression. In: 12th ICMLA, Miami, FL, pp. 135–140 (2013)Google Scholar
  15. 15.
    Georga, E.I., Protopappas, V.C., Polyzos, D.: Prediction of glucose concentration in Type 1 Diabetic patients using support vector regression. In: Proceedings of the 10th IEEE Interantional Conference on Information Technology and Applications in Biomedicine, Corfu, pp. 1–4 (2010)Google Scholar
  16. 16.
    Lu, Y., et al.: The importance of different frequency bands in predicting subcutaneous glucose concentration in Type 1 Diabetic patients. IEEE Trans. Biomed. Eng. 57(8), 1839–1846 (2010)CrossRefGoogle Scholar
  17. 17.
    Novara, C., Pour, N.M., Vincent, T., Grassi, G.: A nonlinear blind identification approach to modeling of diabetic patients. IEEE Trans. Control Syst. Technol. 24(3), 1092–1100 (2016)CrossRefGoogle Scholar
  18. 18.
    Petersen, K., et al.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering (2008)Google Scholar
  19. 19.
    Ouhbi, S., Idri, A., Fernández-Alemán, J.-L., Toval, A.: Requirements engineering education: a systematic mapping study. Requirements Eng. 20(2), 119 (2015)CrossRefGoogle Scholar
  20. 20.
    Sardi, L., Idri, A., Fernández-Alemán, J.-L.: A systematic review of gamification in e-Health. J. Biomed. Inf. 71, 31–48 (2017)CrossRefGoogle Scholar
  21. 21.
    Bachiri, M., Idri, A., Fernández-Alemán, J.-L., Toval, A.: Mobile personal health records for pregnancy monitoring functionalities: analysis and potential. Comput. Methods Programs Biomed. 134, 121–135 (2016)CrossRefGoogle Scholar
  22. 22.
    Andonie, R., Dzitac, I.: How to write a good paper in computer science and how will it be measured by ISI web of knowledge. Int. J. Comput. 5, 432–446 (2010)Google Scholar
  23. 23.
    Eskaf, E.K., Badawi, O., Ritchings, T.: Predicting blood glucose levels in diabetics using feature extraction and artificial neural networks. In: 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, pp. 1–6 (2008)Google Scholar
  24. 24.
    Islam, S., et al.: Peak blood glucose prediction algorithm following a meal intake. In: Canadian Conference on Electrical and Computer Engineering, pp. 579–582 (2007)Google Scholar
  25. 25.
    Naumova, V., et al.: A meta-learning approach to the regularized learning—Case study: blood glucose prediction. Neural Netw. 33, 181–193 (2012)CrossRefGoogle Scholar
  26. 26.
    Preuveneers, D., Berbers, Y.: Mobile phones assisting with health self-care: a diabetes case study. In: The 10th International Conference on HCI with Mobile Devices and Services, pp. 177–186 (2008)Google Scholar
  27. 27.
    McCausland, L., Mareels, I.M.Y.: A probabilistic rule extraction method for an insulin advice algorithm for Type 1 Diabetes Mellitus. In: The 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, vol. 1, pp. 623–626 (2000)Google Scholar
  28. 28.
    Pesl, P., et al.: Live demonstration: an advanced bolus calculator for diabetes management - a clinical and patient platform. IEEE Biomedical Circuits and Systems Conference Proceedings, Lausanne, p. 175 (2014)Google Scholar
  29. 29.
    Ling, S.S.H., Nguyen, H.T.: Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes. IEEE Trans. Inf Technol. Biomed. 15(2), 308–315 (2011)CrossRefGoogle Scholar
  30. 30.
    Chen, S., Feng, L., Rickels, M.R., Peleckis, A., Sokolsky, O., Lee, I.: A data-driven behavior modeling and analysis framework for diabetic patients on insulin pumps. In: 2015 International Conference on Healthcare Informatics, Dallas, TX, pp. 213–222 (2015)Google Scholar
  31. 31.
    Zarkogianni, K., Litsa, E., Vazeou, A., Nikita, K.S.: Personalized glucose-insulin metabolism model based on self-organizing maps for patients with Type 1 Diabetes Mellitus. In: 13th IEEE International Conference on BioInformatics and BioEngineering, Chania, pp. 1–4 (2013)Google Scholar
  32. 32.
    Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks:the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)CrossRefGoogle Scholar
  33. 33.
    Szalay, P., Benyó, Z., Kovács, L.: Long-term prediction for T1DM model during state-feedback control. In: 12th IEEE International Conference on Control and Automation, pp. 311–316 (2016)Google Scholar
  34. 34.
    Faria, B.M., Gonçalves, J., Reis, L.P., Rocha, Á.: A clinical support system based on quality of life estimation. J. Med. Syst. 39, 114 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Software Project Management Research Team, RITCENSIAS, University Mohamed VRabatMorocco
  2. 2.Department of Computer Sciences EMIUniversity Mohamed VRabatMorocco

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