An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966043" target="_blank" >RIV/49777513:23520/22:43966043 - isvavai.cz</a>
Result on the web
<a href="https://diabetes.zcu.cz" target="_blank" >https://diabetes.zcu.cz</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISCC55528.2022.9912918" target="_blank" >10.1109/ISCC55528.2022.9912918</a>
Alternative languages
Result language
angličtina
Original language name
An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models
Original language description
Within this paper a Grammatical Evolution algorithm is exploited to induce personalized and interpretable glucose forecasting models for diabetic patients based on the historical measurements of the glucose, the carbohydrates, and the injected insulin. A real-world data set of Type 1 diabetic patients is used to assess the induced models. The experimental trials show that the performance of extracted models is comparable with that obtained by other state–of–the–art techniques that require a more significant computational effort.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE Symposium on Computers and Communications (ISCC) Proceedings
ISBN
978-1-66549-792-3
ISSN
1530-1346
e-ISSN
—
Number of pages
6
Pages from-to
1-6
Publisher name
IEEE Xplore Conference Publishing
Place of publication
Piscataway
Event location
Rhodes Island, Greece
Event date
Jun 30, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000935799600095