Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11130%2F20%3A10412530" target="_blank" >RIV/00216208:11130/20:10412530 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/20:00341826 RIV/68407700:21730/20:00341826 RIV/00064203:_____/20:10412530

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=4p0VPfPeBK" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=4p0VPfPeBK</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cmpb.2020.105628" target="_blank" >10.1016/j.cmpb.2020.105628</a>

Result language

angličtina

Original language name

Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus

Original language description

Backgroung: Type 1 diabetes is a disease that adversely affects the daily life of a large percentage of people worldwide. Daily glucose levels regulation and useful advices provided to patients regarding their diet are essential for diabetes treatment. For this reason, the interest of the academic community has focused on developing innovative systems, such as decision support systems, based on glucose prediction algorithms. The present work presents the predictive capabilities of ensemble methods compared to individual algorithms while combining each method with compartment models for fast acting insulin absorption simulation. Methods: An approach of combining widely used glycemia prediction algorithms is proposed and three different ensemble methods (Linear, Bagging and Boosting metaregressor) are applied and evaluated on their ability to provide accurate predictions for 30, 45 and 60 minutes ahead prediction horizon. Moreover, glycemia levels, long and short acting insulin dosages and consumed carbohydrates from six type one people with diabetes are used as input data and the results are evaluated in terms of root-mean square error and Clarke error grid analysis. Results: According to results, ensemble methods can provide more accurate glucose concentration in comparison to individual algorithms. Bagging metaregressor, specifically, performed better than individual algorithms in all prediction horizons for small datasets. Bagging ensemble method improved the percentage in zone A according to Clarkes error grid analysis by 4% and in some cases by 9%. Moreover, compartment models are proved to improve results in combination with any method at any prediction horizon. This strengthen the potential practical usefulness of the ensemble methods and the importance of building accurate compartment models.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30202 - Endocrinology and metabolism (including diabetes, hormones)

Project

<a href="/en/project/NV15-25710A" target="_blank" >NV15-25710A: Individual dynamics of glycaemia excursions identification in diabetic patients to improve self managing procedures influencing insulin dosage</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

—

Volume of the periodical

196

Issue of the periodical within the volume

November

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

7

Pages from-to

105628

UT code for WoS article

000580609200049

EID of the result in the Scopus database

2-s2.0-85087338776