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Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis

Author

Listed:
  • Alvis Cabrera

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Ernesto Estremera

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Aleix Beneyto

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Lyvia Biagi

    (Campus Guarapuava, Federal University of Technology–Paraná (UTFPR), Guarapuava 85053-525, Brazil)

  • Iván Contreras

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Josep Antoni Martín-Fernández

    (Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain)

  • Josep Vehí

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain
    Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain)

Abstract

This paper presents an individualized multiple linear regression model based on compositional data where we predict the mean and coefficient of variation of blood glucose in individuals with type 1 diabetes for the long-term (2 and 4 h). From these predictions, we estimate the minimum and maximum glucose values to provide future glycemic status. The proposed methodology has been validated using a dataset of 226 real adult patients with type 1 diabetes (Replace BG (NCT02258373)). The obtained results show a median balanced accuracy and sensitivity of over 90% and 80%, respectively. A information system has been implemented and validated to update patients on their glycemic status and associated risks for the next few hours.

Suggested Citation

  • Alvis Cabrera & Ernesto Estremera & Aleix Beneyto & Lyvia Biagi & Iván Contreras & Josep Antoni Martín-Fernández & Josep Vehí, 2023. "Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis," Mathematics, MDPI, vol. 11(21), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4517-:d:1272768
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    References listed on IDEAS

    as
    1. Alvis Cabrera & Lyvia Biagi & Aleix Beneyto & Ernesto Estremera & Iván Contreras & Marga Giménez & Ignacio Conget & Jorge Bondia & Josep Antoni Martín-Fernández & Josep Vehí, 2023. "Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis," Mathematics, MDPI, vol. 11(5), pages 1-17, March.
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