Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis

Alvis Cabrera, Lyvia Biagi, Aleix Beneyto, Ernesto Estremera, Iván Contreras, Marga Giménez, Ignacio Conget, Jorge Bondia, Josep Antoni Martín-Fernández and Josep Vehí ()
Additional contact information
Alvis Cabrera: 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
Aleix Beneyto: 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
Iván Contreras: Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain
Marga Giménez: Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
Ignacio Conget: Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
Jorge Bondia: Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, 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

Mathematics, 2023, vol. 11, issue 5, 1-17

Abstract: Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDa.

Keywords: compositional data; continuous glucose monitoring; prediction model; time in range; type 1 diabetes (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/5/1241/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/5/1241/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:5:p:1241-:d:1087580

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().