Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)
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AuthorRodríguez Rodríguez, Ignacio; Rodríguez Rodríguez, José Víctor; González Vidal, Aurora; Zamora, Miguel Ángel
Knowledge AreaLenguajes y Sistemas Informáticos
SponsorsThis work has been sponsored by the Spanish Ministry of Economy and Competitiveness through 387 the PERSEIDES (ref. TIN2017-86885-R) and CHIST-ERA (ref. PCIN-2016-010) projects; by MINECO grant BES-2015-071956 and by the European Commission through the H2020-ENTROPY-649849 EU Project. The authors would like to thank to the Endocrinology Department of the Morales Meseguer and Virgen de la Arrixaca hospitals of the city of Murcia (Spain).
Realizado en/conUniversidad Politécnica de Cartagena; Universidad de Murcia
Bibliographic CitationRodríguez-Rodríguez I, Rodríguez J-V, González-Vidal A, Zamora M-Á. Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL). Symmetry. 2019; 11(9):1164. https://doi.org/10.3390/sym11091164
KeywordsContinuous glucose monitoring
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 ...
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