Analysis of the performance of genetic programming on the blood glucose level prediction challenge 2020

David Joedicke, Oscar Garnica, Gabriel Kronberger, J. Manuel Colmenar, Stephan Winkler, J. Manuel Velasco, Sergio Contador, J. Ignacio Hidalgo

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

In this paper we present results for the Blood Glucose Level Prediction Challenge for the Ohio2020 dataset. We have used four variants of genetic programming to build white-box models for predicting 30 minutes and 60 minutes ahead. The results are compared to classical methods including multi-variate linear regression, random forests, as well as two types of ARIMA models. Notably, we have included future values of bolus and basal into some of the models because we assume that these values can be controlled. Additionally, we have used a convolution filter to smooth the information in the bolus volume feature. We find that overall tree-based GP performs well and better than multi-variate linear regression and random forest, while ARIMA models performed worst on the here analyzed data.

Original languageEnglish
Pages (from-to)141-145
Number of pages5
JournalCEUR Workshop Proceedings
Volume2675
Publication statusPublished - 2020
Event5th International Workshop on Knowledge Discovery in Healthcare Data, KDH 2020 - Virtual, Santiago de Compostela, Spain
Duration: 29 Aug 202030 Aug 2020

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