Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios



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Contador, Sergio and Colmenar, J. Manuel and Garnica Alcázar, Oscar and Velasco Cabo, José Manuel and Hidalgo Pérez, José Ignacio (2021) Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios. Genetic Programming and Evolvable Machines . ISSN 1389-2576

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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.

Item Type:Article
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CRUE-CSIC (Acuerdos Transformativos 2021)

Uncontrolled Keywords:Grammatical evolution, Multi-objective optimization, Glucose prediction, Diabetes
Subjects:Sciences > Computer science
Sciences > Computer science > Computer programming
ID Code:70578
Deposited On:18 Feb 2022 10:09
Last Modified:18 Feb 2022 10:10

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