RT Journal Article
T1 Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios
A1 Contador, Sergio
A1 Colmenar, J. Manuel
A1 Garnica Alcázar, Antonio Óscar
A1 Velasco Cabo, José Manuel
A1 Hidalgo Pérez, José Ignacio
AB In this paper we investigate the benefts of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the root-mean-square error and an ad-hoc ftness 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 in diabetic patients. In this work, we use two datasets to analyse two diferent scenarios: What-if and Agnostic, the most common in daily clinical practice. In the What-if scenario, where future events are evaluated, results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis by reducing the number of dangerous mispredictions. In the Agnostic situation, with no available information about future events, results suggest that we can obtain good predictions with only information from the previous hour for both Grammatical Evolution and Multi-Objective Grammatical Evolution.
PB Springer Nature
SN 1389-2576
YR 2021
FD 2021-11-18
LK https://hdl.handle.net/20.500.14352/4724
UL https://hdl.handle.net/20.500.14352/4724
LA eng
NO CRUE-CSIC (Acuerdos Transformativos 2021)
NO Ministerio de Ciencia e Innovación (MICINN)/FEDER
NO Comunidad de Madrid/FEDER
DS Docta Complutense
RD 6 abr 2025