Kumar DA. Analysis of computational intelligence techniques for diabetes mellitus prediction. Neural Comput Appl. 2018;30(12):3837–45.

Article  Google Scholar 

Harvey RA, Wang Y, Grosman B, Percival MW, Bevier W, Finan DA, Zisser H, Seborg DE, Jovanovic L, Doyle FJ, Dassau E. Quest for the artificial pancreas: combining technology with treatment. IEEE Eng Med Biol Mag. 2010;29(2):53–62. https://doi.org/10.1109/MEMB.2009.935711.

Article  Google Scholar 

Yang J, Li L, Shi Y, Xie X. An arima model with adaptive orders for predicting blood glucose concentrations and hypoglycemia. IEEE J Biomed Health Inf. 2019;23(3):1251–60. https://doi.org/10.1109/JBHI.2018.2840690.

Article  Google Scholar 

Danne T, Nimri R, Battelino T. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40(12):1631–40. https://doi.org/10.2337/dc17-1600.

Article  Google Scholar 

Virginie F, Nuno MG, Nuno P, Imen M. Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction - a systematic literature review. Artif Intel Med. 2021;118:102–20. https://doi.org/10.1016/j.artmed.2021.102120.

Article  Google Scholar 

Georga EI, Protopappas VC, Ardigò D, Marina M, Zavaroni I, Polyzos D, Fotiadis DI. Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE J Biomed Health Inf. 2013;17(1):71–81. https://doi.org/10.1109/TITB.2012.2219876.

Article  Google Scholar 

Sun Q, Jankovic MV, Bally L, Mougiakakou SG. Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network. Paper presented at the 14th Symposium on Neural Networks and Applications (NEUREL), Belgrade: IEEE, 2018;1–5

Woldaregay AZ, 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 Intel Med. 2019;98:109–34. https://doi.org/10.1016/j.artmed.2019.07.007.

Article  Google Scholar 

Świa̧tek J, JM Tomczak E,. Gaussian process regression with categorical inputs for predicting the blood glucose level. Adv Syst Sci. 2017;539:98–108. https://doi.org/10.1007/978-3-319-48944-5_10.

Article  Google Scholar 

Rasmussen CE, Williams CKI. Gaussian processes for machine learning. In: Adaptive computation and machine learning. Cambridge: The MIT Press; 2005.

Google Scholar 

Contador Pachón S, Botella Serrano M, Garnica Alcázar O, Velasco Cabo JM, Aramendi Zurimendi A, Rodríguez Martínez R, Maqueda Villaizán E, Hidalgo Pérez JI. Identification of blood glucose patterns in patients with type 1 diabetes using continuous glucose monitoring and clustering technique. Endocrinol Diabet y Nutrición. 2021;68(3):170–4. https://doi.org/10.1016/j.endien.2021.06.001.

Article  Google Scholar 

Xinyu Z, Xia Y, Zhanhu Z, Hongru L, Jingyi L, Jian Z. A state-by-state online transfer learning framework with incremental clustering for blood glucose prediction. Biomed Signal Process Control. 2023;86(3):105200. https://doi.org/10.1016/j.bspc.2023.105200.

Article  Google Scholar 

Qu H, Li J, Zhang Y. Long Short-term Memory Network Prediction Model Based on Fuzzy Time Series. Paper presented at the 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), Dalian, China, 2020;417–421 2020. https://doi.org/10.1109/ICAIIS49377.2020.9194902.

Chandra B, Gupta M, Gupta MP. A multivariate time series clustering approach for crime trends prediction. Paper presented at the 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore, 2008;892–896 2008. https://doi.org/10.1109/ICSMC.2008.4811393.

Almuammar, M., Fasli, M.: Deep Learning for Non-stationary Multivariate Time Series Forecasting. Paper presented at the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2097–2106 2019 (2019). https://doi.org/10.1109/BigData47090.2019.9006192.

Kim D-K, Kim K. A convolutional transformer model for multivariate time series prediction. IEEE Access. 2022;10:101319–29. https://doi.org/10.1109/ACCESS.2022.3203416.

Article  Google Scholar 

Georga EI, Protopappas VC, Ardigò D, et al. Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE J Biomed Health Inf. 2013;17(1):71–81. https://doi.org/10.1109/TITB.2012.2219876.

Article  Google Scholar 

Zhao Q, Zhu J, Shen X, Lin C et al. Chinese diabetes datasets for data-driven machine learning. Scientific Data. 2023;10(35), https://doi.org/10.1038/s41597-023-01940-7

Bondia J, Vehí J, Palerm CC, Herrero P. El páncreas artificial: Control automático de infusión de insulina en diabetes mellitus tipo 1. Revista Iberoamericana de Automática e Informática Ind RIAI. 2010;7(2):5–20. https://doi.org/10.1016/S1697-7912(10)70021-2.

Article  Google Scholar 

Yuchai W, Xiabi L, Yi W, Lunhao G, Qiming C, Murong W. Icgt: A novel incremental clustering approach based on GMM tree. Data Knowl Eng. 2018;117:71–86. https://doi.org/10.1016/j.datak.2018.07.003.

Article  Google Scholar 

Huang D, Wang C-D, Peng H, Lai J, Kwoh C-K. Enhanced ensemble clustering via fast propagation of cluster-wise similarities. IEEE Trans Syst Man Cybern Syst. 2021;51(1):508–20. https://doi.org/10.1109/TSMC.2018.2876202.

Article  Google Scholar 

IAM- ON N, BOONGOEN T, GARRETT SM. Refining pairwise similarity matrix for cluster ensemble problem with cluster relations. Proceedings of the 11th International Conference on Discovery Science, Budapest,222–233 ; 2008. Berlin, Heidelberg: Springer

GARRETT S. A link-based cluster ensemble method for improved gene expression data analysis. Bioinformatics. 2010;26(12):1513–9.

Article  Google Scholar 

Liang Y, Ren Z, Wu Z, Li J. Scalable spectral ensemble clustering via building representative co-association matrix. Neurocomputing. 2020;390:158–67.

Article  Google Scholar 

Huang D, Lai J-H, Wang C-D. Robust ensemble clustering using probability trajectories. IEEE Trans Knowl Data Eng. 2016;28(5):1312–26. https://doi.org/10.1109/TKDE.2015.2503753.

Article  Google Scholar 

Hongyan X, Xuezhong Q, Shibing Z. Ensemble clustering algorithm based on weighted super cluster. J Front Comput Sci Technol. 2021;15(12):2362–73.

Google Scholar 

Kang L, Chen R-S, Xiong N, Chen Y-C, Hu Y-X, Chen C-M. Selecting hyper-parameters of gaussian process regression based on non-inertial particle swarm optimization in internet of things. IEEE Access. 2019;7:59504–13. https://doi.org/10.1109/ACCESS.2019.2913757.

Article  Google Scholar 

Han J, Zhang X-P, Wang F. Gaussian process regression stochastic volatility model for financial time series. IEEE J Sel Top Signal Process. 2016;10(6):1015–28. https://doi.org/10.1109/JSTSP.2016.2570738.

Article  Google Scholar 

Li Y, Ma Z, Pan Z, Liu N, You X. Prophet model and gaussian process regressionbased user traffic prediction in wireless networks. Sci China Inf Sci. 2020;63(4):124–61. https://doi.org/10.1007/s11432-019-2695-6.

Article  Google Scholar 

Jaini P et al. Online baysian transfer learning for sequential data modeling, 2017;20

Marling C, Bunescu R. Ohiot1dm 2020; http://smarthealth.cs.ohio.edu/OhioT1DM-dataset.html

Chai T, Draxler RR. Root mean square error (rmse) or mean absolute error (mae)? - arguments against avoiding rmse in the literature. Geosci Model Dev. 2014;7(3):1247–50. https://doi.org/10.5194/gmd-7-1247-2014.

Article  Google Scholar 

Ajjan RA, Cummings MH, Jennings P. Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: implications for clinical practice. Diabet Vasc Dis Res. 2018;215(3):175–84. https://doi.org/10.1177/1479164118756240.

Article  Google Scholar 

Clarke., W.L. The original clarke error grid analysis (ega). Diabet Technol Ther. 2005;7(5):776–9. https://doi.org/10.1089/dia.2005.7.776.

Article  Google Scholar 

Zhao Q, Zhu J, Wang C, Rao W. Diabetes datasets, shanghait1dm and shanghait2dm. figshare. 2022. https://doi.org/10.6084/m9.figshare.c.6310860.

Article  Google Scholar 

Montaser E, Díez J-L, Rossetti P, Rashid M, Cinar A, Bondia J. Seasonal local models for glucose prediction in type 1 diabetes. IEEE J Biomed Health Inf. 2020;24(7):2064–72. https://doi.org/10.1109/JBHI.2019.2956704.

Article  Google Scholar 

Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng. 2007;54(5):931–7. https://doi.org/10.1109/TBME.2006.889774.

Article  Google Scholar 

Finan D, Doyle F, Palerm C, et al. Experimental evaluation of a recursive model identification technique for type 1 diabetes. Diabet Sci Technol. 2009;3(5):1192–202.

Article  Google Scholar 

Turksoy K, Bayrak E, Quinn L, et al. Hypoglycemia early alarm systems based on multivariable models. Ind Eng Chem Res. 2013;52(35):12329–36.

Article  Google Scholar 

Eren-Oruklu M, Cinar A, Rollins D, et al. Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms. Automatica. 2012;48(8):1892–7.

Article  MathSciNet  Google Scholar 

Montaser E, Díez J, Bondia J, et al. Stochastic seasonal models for glucose prediction in the artificial pancreas. Diabet Sci Technol. 2017;11(6):1124–31.

Article  Google Scholar 

Sfetsos A, Siriopoulos C. Time series forecasting with a hybrid clustering scheme and pattern recognition. IEEE Trans Syst Man Cybern Part A Syst Humans. 2004;34(3):399–405. https://doi.org/10.1109/TSMCA.2003.822270.

Article  Google Scholar 

Pappada S, Cameron B, Rosman P, et al. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabet Technol Ther. 2011;13(2):135–41.

Article  Google Scholar 

Daskalaki E, Prountzou A, Diem P, et al. Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. Diabet Technol Ther. 2012;14(2):168–74.

Article  Google Scholar 

Georga E, Príncipe J, Fotiadis D. Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters. Med Biol Eng Comput. 2019;57(1):27–46.

Article  Google Scholar