Predicting blood glucose using an LSTM neural network

• Autorzy: El Idrissi Touria , Idri Ali , Abnane Ibtissam , Bakkoury Zohra

Identyfikatory

DOI

10.15439/2019F159

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network model for blood glucose prediction. The model is a sequential one using a Long- Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model's parameters in order to identify the best variant of the model. The performance of the proposed model measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM model and an autoregressive (AR) model. The results show that our model is significantly more accurate; in fact, our LSTM model outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by thWilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.

Słowa kluczowe

EN

biochemistry; biomedical measurements; blood; diseases; artificial neural networks; medical computing; neural nets; statistical testing; sugar

PL

biochemia; pomiary biomedyczne; krew; choroba; sztuczne sieci neuronowe; informatyka medyczna; sieci neuronowe; testy statystyczne; cukier

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 35--41
• Opis fizyczny: Bibliogr. 30 poz., wz., tab., wykr.

Twórcy

autor

El Idrissi Touria

• Department of Computer Sciences, EMI, University Mohamed V, Rabat, Morocco

autor

Idri Ali

• Software Project Management Research, Team ENSIAS, University Mohamed V, Rabat, Morocco

autor

Abnane Ibtissam

• Software Project Management Research, Team ENSIAS, University Mohamed V, Rabat, Morocco

autor

Bakkoury Zohra

• Department of Computer Sciences, EMI, University Mohamed V, Rabat, Morocco

Bibliografia

• 1. N. Esfandiari, M. R. Babavalian, A. M. E. Moghadam & V. K. Tabar, "Knowledge discovery in medicine: Current issue and future trend", In Expert Systems with Applications, vol. 41, no. 9, pp. 4434-4463, 2014, https://doi.org/10.1016/j.eswa.2014.01.011
• 2. H. Benhar, A. Idri and J.-L. Fernández-Alemán, "Data preprocessing for decision making in medical informatics: potential and analysis." World Conference on Information Systems and Technologies. Springer, Cham, 2018, pp. 1208-1218, https://doi.org/10.1007/978-3-319-77712-2_116 .
• 3. T. El Idrissi, A. Idri, and Z. Bakkoury, "Data Mining Techniques in Diabetes Self-management: A Systematic Map", In World Conference on Information Systems and Technologies, Springer, Cham, 2018, pp. 1142-1152, https://doi.org/10.1007/978-3-319-77712-2_109 .
• 4. I. Kadi, A. Idri and J.-L. Fernandez-Aleman. "Knowledge discovery in cardiology: A systematic literature review”. International Journal of Medical Informatics, vol. 97, pp. 12-32, 2017, https://doi.org/10.1016/j.ijmedinf.2016.09.005.
• 5. A. Idri, I. Chlioui and B. EL Ouassif, "A systematic map of data analytics in breast cancer”, In Proceedings of the Australasian Computer Science Week Multiconference, ACM , 2018, p. 26, https://doi.org/10.1145/3167918.3167930 .
• 6. R. Billous, R. Donnally, “Handbook of Diabetes”, Blackwell , 2010.
• 7. T. EL Idrissi, A. Idri and Z. Bakkoury, “Systematic map and review of predictive techniques in diabetes self-management”, International Journal of Information Management, vol. 46, pp. 263-277, 2019, https://doi.org/10.1016/j.ijinfomgt.2018.09.011.
• 8. Q. Sun, M. V. Jankovic, L. Bally and S. G. Mougiakakou, "Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network," In Symposium on Neural Networks and Applications (NEUREL), Belgrade, 2018, pp. 1-5, https://doi.org/10.1109/NEUREL.2018.8586990
• 9. H. N. Mhaskar, S. V. Pereverzyev, and M. D. van der Walt, “A Deep Learning Approach to Diabetic Blood Glucose Prediction,” Front. Appl. Math. Stat., vol. 3, no. July, pp. 1–11, 2017, https://doi.org/10.3389/fams.2017.00014
• 10. Y. Wang, J. Zhou, K. Chen, Y. Wang and L. Liu, "Water quality prediction method based on LSTM neural network," 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, 2017, pp. 1-5, https://doi.org/10.1109/ISKE.2017.8258814
• 11. N. Kim, M. Kim and J. K. Choi, "LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences," 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), Nara, 2018, pp. 136-137, https://doi.org/10.1109/GCCE.2018.8574484
• 12. M. Roondiwala, H. Patel, and S. Varma, “Predicting Stock Prices Using LSTM,” International Journal of Science and Research (IJSR), vol.6, no. 4, pp.1754-1756, 2017.
• 13. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, https://doi.org/10.1162/neco.1997.9.8.1735
• 14. K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink and J. Schmidhuber, "LSTM: A Search Space Odyssey," in IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222-2232, Oct. 2017, https://doi.org/10.1109/TNNLS.2016.2582924 .
• 15. Y. Lu, A. V. Gribok, W. K. Ward, and J. Reifman.. "The Importance of Different Frequency Bands in Predicting Subcutaneous Glucose Concentration in Type 1 Diabetic Patients," in IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, pp. 1839-1846, Aug. 2010, https://doi.org/10.1109/TBME.2010.2047504
• 16. C. Novara, N. M. Pour, T. Vincent, G. Grassi, "A Nonlinear Blind Identification Approach to Modeling of Diabetic Patients," in IEEE Transactions on Control Systems Technology, vol. 24, no. 3, pp. 1092- 1100, May 2016, https://doi.org/10.1109/TCST.2015.2462734
• 17. Q. Wang S. Harsh, P. Molenaar, and K. Freeman,."Developing personalized empirical models for Type-I diabetes: An extended Kalman filter approach," American Control Conference, IEEE, 2013, pp. 2923-2928, https://doi.org/10.1109/ACC.2013.6580278.
• 18. K. Zarkogianni, A.Vazeou, S. G. Mougiakakou, A. Prountzou and K. S. Nikita."An Insulin Infusion Advisory System Based on Autotuning Nonlinear Model-Predictive Control," in IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, pp. 2467-2477, Sept. 2011, https://doi.org/10.1109/TBME.2011.2157823.
• 19. F. Allam, Z. Nossair, H. Gomma, I. Ibrahim, and M. Abd-el Salam. "Prediction of subcutaneous glucose concentration for type-1 diabetic patients using a feed forward neural network," The Int. Conf. on Computer Engineering & Systems, Cairo, 2011, pp. 129-133, https://doi.org/10.1109/ICCES.2011.6141026.
• 20. N. Mathiyazhagan, H. B. Schechter, "Soft computing approach for predictive blood glucose management using a fuzzy neural network," IEEE Conf. on Norbert Wiener in the 21st Century (21CW), Boston, MA, 2014, pp. 1-3, https://doi.org/10.1109/NORBERT.2014.6893906.
• 21. R. Bunescu, N. Struble, C. Marling, J. Shubrook, and F. Schwartz, "Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression," In 2013 12th International Conference on Machine Learning and Applications, vol. 1, pp. 135-140. IEEE, 2013, https://doi.org/10.1109/ICMLA.2013.30.
• 22. E. I. Georga, V. C. Protopappas, D. Polyzos, "Prediction of glucose concentration in type 1 diabetic patients using support vector regression," Proceedings of the 10th IEEE Int. Conf. on Information Technology and Applications in Biomedicine, Corfu, 2010, pp. 1-4, https://doi.org/10.1109/ITAB.2010.5687764
• 23. Diabetes Research in Children Network (DirecNet). Available online at: http://direcnet.jaeb.org/Studies.aspx [Ap. 1,2019]
• 24. M. Hosni, A. Idri and A. Abran, "Investigating heterogeneous ensembles with filter feature selection for software effort estimation." In Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement, ACM, 2017, pp. 207-220, https://doi.org/10.1145/3143434.3143456
• 25. A. Idri, I. Abnane and A. Abran. "Missing data techniques in analogy- based software development effort estimation." Journal of Systems and Software, vol. 117, pp. 595-611, 2016, https://doi.org/10.1016/j.jss.2016.04.058.
• 26. X. Chen and X. Lin, "Big Data Deep Learning: Challenges and Perspectives," in IEEE Access, vol. 2, pp. 514-525, 2014, https://doi.org/10.1109/ACCESS.2014.2325029.
• 27. N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik and A. Swami, "The Limitations of Deep Learning in Adversarial Settings," 2016 IEEE European Symposium on Security and Privacy (EuroS&P), Saarbrucken, 2016, pp. 372-387, https://doi.org/10.1109/EuroSP.2016.36.
• 28. S. Ouhbi, A. Idri, J. L. Fernández-Alemán and A. Toval, "Mobile personal health records for cardiovascular patients," 2015 Third World Conference on Complex Systems (WCCS), Marrakech, 2015, pp. 1-6, https://doi.org/10.1109/ICoCS.2015.7483226
• 29. M. Bachiri, A. Idri, J.-L. Fernández-Alemán, A. Toval. "Mobile personal health records for pregnancy monitoring functionalities: Analysis and potential." Computer methods and programs in biomedicine, vol. 134, pp. 121-135, 2016, https://doi.org/10.1016/j.cmpb.2016.06.008.
• 30. Sergeev, A., & Del Balso, M. (2018). Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint https://arxiv.org/abs/1802.05799.

Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).