Abstract
In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of grammatical evolution. In particular, we continue with previous research about finding expressions to model the glucose levels in blood of diabetic patients. We use here a multi-objective Grammatical Evolution approach based on NSGA-II algorithm, considering the root mean squared error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions. Experimental results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis reducing the number of dangerous mispredictions.
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Notes
- 1.
The International Diabetes Federation estimates around 415 million diabetic patients [17] (rising from 108 million since 1980), which is about 8–10% of prevalence on adults over 18 years, and it is the seventh leading cause of death in 2016, with 1.6 million deaths directly caused by diabetes and 2.2 million additional deaths attributable to high blood glucose.
- 2.
When constructing prediction models that help in the recommendation, we can use variables (features) that include the information involved in the recommendation process and thus be able to use them effectively. This does not mean that we use information from the future.
References
Bakhtiani, P.A., Zhao, L.M., El Youssef, J., Castle, J.R., Ward, W.K.: A review of artificial pancreas technologies with an emphasis on bi-hormonal therapy. Diab. Obes. Metab. 15(12), 1065–1070 (2013)
Clarke, W., Cox, D., Gonder-Frederick, L., Carter, W., Pohl, S.: Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diab. Care 10(5), 622–628 (1987)
Contador, S., Hidalgo, J.I., Garnica, O., Velasco, J.M., Lanchares, J.: Can clustering improve glucose forecasting with genetic programming models? In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1829–1836. ACM (2019)
Contreras, I., Oviedo, S., Vettoretti, M., Visentin, R., Vehí, J.: Personalized blood glucose prediction: a hybrid approach using grammatical evolution and physiological models. PLoS ONE 12(11), e0187754 (2017)
Dassau, E., et al.: Artificial pancreatic beta-cell protocol for enhanced model identification. In: Diabetes, vol. 58, pp. A105–A106. American Diabetes Association, Alexandria (2009)
De Falco, I., Della Cioppa, A., Koutny, T., Krcma, M., Scafuri, U., Tarantino, E.: Genetic programming-based induction of a glucose-dynamics model for telemedicine. J. Netw. Comput. Appl. 119, 1–13 (2018)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)
Gevers, M.: Identification for control: from the early achievements to the revival of experiment design*. Eur. J. Control 11(4), 335–352 (2005)
Hemberg, E., Ho, L., O’Neill, M., Claussen, H.: A comparison of grammatical genetic programming grammars for controlling femtocell network coverage. Genet. Program Evolvable Mach. 14(1), 65–93 (2013)
Hidalgo, J.I., et al.: Identification of models for glucose blood values in diabetics by grammatical evolution. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 367–393. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_15
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(9), 142 (2017)
Lourenço, N., Colmenar, J.M., Hidalgo, J.I., Garnica, Ó.: Structured grammatical evolution for glucose prediction in diabetic patients. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1250–1257. ACM (2019)
Man, C.D., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C.: The UVA/PADOVA type 1 diabetes simulator: new features. J. Diab. Sci. Technol. 8(1), 26–34 (2014)
Mays, L.: Diabetes mellitus standards of care. Nurs. Clin. North Am. 50(4), 703–711 (2015). Pathophysiology and Care Protocols for Nursing Management
Messori, M., Toffanin, C., Favero, S.D., Nicolao, G.D., Cobelli, C., Magni, L.: Model individualization for artificial pancreas. Comput. Methods Programs Biomed. 171, 133–140 (2016)
Moreno-Salinas, D., Besada-Portas, E., López-Orozco, J., Chaos, D., de la Cruz, J., Aranda, J.: Symbolic regression for marine vehicles identification. IFAC-PapersOnLine 48(16), 210–216 (2015)
Ogurtsova, K., et al.: IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diab. Res. Clin. Pract. 128, 40–50 (2017)
Oviedo, S., Contreras, I., Quirós, C., Giménez, M., Conget, I., Vehi, J.: Risk-based postprandial hypoglycemia forecasting using supervised learning. Int. J. Med. Inform. 126, 1–8 (2019)
Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0055930
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. 54(5), 931–937 (2007)
Steil, G., et al.: Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia. Diabetologia 48(9), 1833–1840 (2005)
Sun, Q., et al.: A dual mode adaptive basal-bolus advisor based on reinforcement learning. IEEE J. Biomed. Health Inform. 23, 2633–2641 (2018)
Velasco, J.M., et al.: Enhancing grammatical evolution through data augmentation: application to blood glucose forecasting. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 142–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55849-3_10
Weissberg-Benchell, J., Antisdel-Lomaglio, J., Seshadri, R.: Insulin pump therapy. Diab. Care 26(4), 1079–1087 (2003)
Yu, C., Zhao, C.: Rapid model identification for online glucose prediction of new subjects with type 1 diabetes using model migration method. IFAC Proc. Vol. 47(3), 2094–2099 (2014). 19th IFAC World Congress
Acknowledgments
This work has been supported by: Fundación Eugenio Rodriguez Pascual 2019 grant -Desarrollo de sistemas adaptativos y bioinspirados para el control glucémico con infusores subcutáneos continuos de insulina y monitores continuos de glucosa (Development of adaptive and bioinspired systems for glycaemic control with continuous subcutaneous insulin infusors and continuous glucose monitors); The Spanish Ministerio de Innovación Ciencia y Universidad - grant RTI2018-095180-B-I00; Madrid Regional Government-FEDER grants B2017/BMD3773 (GenObIA-CM); and Y2018/NMT-4668 (Micro-Stress- MAP-CM).
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Contador, S., Colmenar, J.M., Garnica, O., Hidalgo, J.I. (2020). Short and Medium Term Blood Glucose Prediction Using Multi-objective Grammatical Evolution. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_32
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