Abstract
A mathematical model of glucose process is generated using symbolic regression. Considering a record of data of glucose and insulin of a patient with type II diabetes, a data driven model is generated. Neural networks are black boxes and symbolic regression can generate equations that express explicit a relationship between input variables with respect to the output response. This model is a personalized version of the metabolism of the patient and different treatments can be considered using this model.
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Acknowledgments
I thank to Griselda Quiroz from Universidad Autónoma de Nuevo León, and Instituto Potosino de Investigaciones Científicas y Tecnológicas (IPICYT) a by its contribution and experimental data provided for this paper.
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Torres-Treviño, L.M. (2017). Mathematical Model of Glucose Metabolism by Symbolic Regression \(\alpha \) \(\beta \) . In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_15
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DOI: https://doi.org/10.1007/978-3-319-62428-0_15
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