Feature selection for blood glucose level prediction in type 1 Diabetes Mellitus by using the Sequential Input Selection Algorithm (SISAL)

Rodríguez Rodríguez, Ignacio; Rodríguez, José Víctor; González Vidal, Aurora; Zamora Izquierdo, Miguel Ángel

Publication:
Feature selection for blood glucose level prediction in type 1 Diabetes Mellitus by using the Sequential Input Selection Algorithm (SISAL)

dc.contributor.author	Rodríguez Rodríguez, Ignacio
dc.contributor.author	Rodríguez, José Víctor
dc.contributor.author	González Vidal, Aurora
dc.contributor.author	Zamora Izquierdo, Miguel Ángel
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.contributor.other	Facultades de la UMU::Facultad de Informática
dc.date.accessioned	2026-02-18T11:19:38Z
dc.date.available	2026-02-18T11:19:38Z
dc.date.copyright	© 2019 by the authors
dc.date.issued	2019-09-14
dc.description.abstract	Feature selection is a primary exercise to tackle any forecasting task. Machine learning algorithms used to predict any variable can improve their performance by lessening their computational effort with a proper dataset. Anticipating future glycemia in type 1 diabetes mellitus (DM1) patients provides a baseline in its management, and in this task, we need to carefully select data, especially now, when novel wearable devices offer more and more information. In this paper, a complete characterization of 25 diabetic people has been carried out, registering innovative variables like sleep, schedule, or heart rate in addition to other well-known ones like insulin, meal, and exercise. With this ground-breaking data compilation, we present a study of these features using the Sequential Input Selection Algorithm (SISAL), which is specially prepared for time series data. The results rank features according to their importance, regarding their relevance in blood glucose level prediction as well as indicating the most influential past values to be taken into account and distinguishing features with person-dependent behavior from others with a common performance in any patient. These ideas can be used as strategies to select data for predicting glycemia depending on the availability of computational power, required speed, or required accuracy. In conclusion, this paper tries to analyze if there exists symmetry among the different features that can affect blood glucose levels, that is, if their behavior is symmetric in terms of influence in glycemia.
dc.format	application/pdf
dc.format.extent	17
dc.identifier.citation	Rodríguez-Rodríguez, I., Rodríguez, J. V., González-Vidal, A., & Zamora, M. Á. (2019). Feature selection for blood glucose level prediction in type 1 diabetes mellitus by using the sequential input selection algorithm (SISAL). Symmetry, 11(9), 1164.
dc.identifier.doi	https://doi.org/10.3390/sym11091164
dc.identifier.eissn	2073-8994
dc.identifier.uri	http://hdl.handle.net/10201/207481
dc.language	eng
dc.publisher	MDPI
dc.relation	This work has been sponsored by the Spanish Ministry of Economy and Competitiveness through the PERSEIDES (ref. TIN2017-86885-R) and CHISTERA (ref. PCIN-2016-010) projects; by MINECO grant BES-2015-071956 and by the European Commission through the H2020-ENTROPY-649849 EU Project.
dc.relation.publisherversion	https://www.mdpi.com/2073-8994/11/9/1164
dc.rights	Attribution 4.0 International	*
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Continuous glucose monitoring
dc.subject	Wearable devices
dc.subject	Features selection
dc.subject	Time series
dc.subject	Machine learning
dc.subject.ods	Objetivo 3: Salud
dc.title	Feature selection for blood glucose level prediction in type 1 Diabetes Mellitus by using the Sequential Input Selection Algorithm (SISAL)
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion
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