Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms

Redy Indrawan, Siti Saadah, Prasti Eko Yunanto

DOI: https://doi.org/10.23917/khif.v7i2.14629

Abstract

Diabetes Mellitus is one of the preeminent causes of death to date. Effective procedures are necessary to prevent diabetes and avoid complications that may cause early death. A common approach is to control patient blood glucose, which necessitates a periodic measurement of blood glucose concentration. This study developed a blood glucose prediction system using a convolutional long short-term memory (Conv-LSTM) algorithm. Conv-LSTM is a variation of LSTM algorithms that are suitable for use in time series problems. Conv-LSTM overcomes the lack in the LSTM algorithm because the latter algorithm cannot access the content of previous memory cells when its output gate has closed. We tested the algorithm and varied the experiment to check the effect of the cross-validation ratio between 70:30 and 80:20. The study indicates that the cross-validation using a ratio of 70:30 data split is more stable compared to one with 80:20 data split. The best result shows a measure of 21.44 in RMSE and 8.73 in MAE. With the application of conv-LSTM using correct parameters and selected data split, our experiment attains accuracy comparable to the regular LSTM.

Keywords

diabetes mellitus; blood glucose; recurrent neural network; long short-term memory

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References

WHO NCD Management-Screening, Diagnosis, and Treatment, "Global Report on Diabetes", 2016. Isbn, 9789241565257. Available online at: https://www.who.int/publications/i/item/9789241565257

Q. Wang, S. Harsh, P. Molenaar, and K. Freeman, "Developing personalized empirical models for Type-I diabetes: An extended Kalman filter approach," 2013. American Control Conference, Washington, DC, 2013, pp. 2923-2928, DOI: 10.1109/ACC.2013.6580278.

Mhaskar, Hrushikesh & Pereverzyev, Sergei & Van der Walt, Maria, "A Deep Learning Approach to Diabetic Blood Glucose Prediction," 2017. Frontiers in Applied Mathematics and Statistics. 3. 10.3389/fams.2017.00014.

Novara, Carlo & Pour, Nima & Vincent, Tyrone & Grassi, Giorgio, "A Nonlinear Blind Identification Approach to Modeling of Diabetic Patients" 2015. IEEE Transactions on Control Systems Technology. 19. 1-1. 10.1109/TCST.2015.2462734.

Q. Sun, M. V. Jankovic, L. Bally, and S. G. Mougiakakou, "Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network," 2018. 14th Symposium on Neural Networks and Applications (NEUREL), Belgrade, 2018, pp. 1-5.

T. El. Idriss, A. Idri, I. Abnane, and Z. Bakkoury, "Predicting Blood Glucose using an LSTM Neural Network," 2019. Federated Conference on Computer Science and Information Systems (FedCSIS), Leipzig, Germany, 2019, pp. 35-41.

Hochreiter, Sepp & Schmidhuber, Jürgen, Long Short-term Memory. Neural computation, 1997, 9. 1735-80.

Shi, X.; Chen, Z.; Wang, H.; Yeung, D.; Wong, W.; Woo, W. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In Proceedings of the 28th International Conference on Neural Information Processing Systems; MIT Press: Montreal, QC, Canada, 2015; pp. 802–810.

Rahman, Md. M.; Siddiqui, Fazlul H., "An Optimized Abstractive Text Summarization Model Using Peephole Convolutional LSTM", 2019 Symmetry 11, no. 10: 1290

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.

Chung, H., & Shin, K. S, "Genetic algorithm-optimized long short-term memory network for stock market prediction," 2018. Sustainability, 10(10), 3765

Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y. Deep Learning; MIT Press: Cambridge, MA, USA, 2016; pp. 373–418

Schmidhuber, J.; Hochreiter, S. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.

Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to forget: Continual prediction with LSTM. Neural Comput. 1999, 12, 2451–2471.

Kim, Y.; Roh, J.H.; Kim, H. Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks. Sustainability 2017, 10, 34.

Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., Woo, W., "Convolutional LSTM network: a machine learning approach for precipitation nowcasting" 2015. Adv. Neural Inf. Process. Syst. 2015 (January), 802–810 Jun

JAEB Center for Health Research. Available online at: https://public.jaeb.org/datasets/diabetes

T. El Idrissi, A. Idri, and Z. Bakkoury, “Systematic map and review of predictive techniques in diabetes self-management”, International Journal of Information Management, 2019; vol. 46, pp. 263-277

E. Daskalaki, A. Prountzou, P. Diem, and S. G. Mougiakakou,

“Real-Time Adaptive Models for the Personalized Prediction of

Glycemic Profile in Type 1 Diabetes Patients,” Diabetes Technol.

Ther., vol. 14, no. 2, pp. 168–174, 2012.

Medar, Ramesh & Rajpurohit, Vijay & Rashmi, B. (2017). Impact of Training and Testing Data Splits on Accuracy of Time Series Forecasting in Machine Learning. 1-6. 10.1109/ICCUBEA.2017.8463779.

Rahman, Motiur et al. “A deep learning approach based on convolutional LSTM for detecting diabetes.” Computational biology and chemistry vol. 88 (2020): 107329. doi:10.1016/j.compbiolchem.2020.107329

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