Ensemble Based Approach for Time Series Classification in Metabolomics

Michael Netzer, Friedrich Hanser, Marc Breit, K.M. Weinberger, Christian Baumgartner, Daniel Baumgarten

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. OBJECTIVES: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. METHODS: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. RESULTS: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation. CONCLUSION: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.

Original languageEnglish
Title of host publicationdHealth 2019 - From eHealth to dHelath
Subtitle of host publicationProceedings of the 13th Health Informatics Meets Digital Health Conference
EditorsDieter Hayn, Alphons Eggerth, Günter Schreier
PublisherIOS Press
Pages89-96
Number of pages8
ISBN (Print)978-1-61499-974-4
DOIs
Publication statusPublished - 2019
Event13th Annual Conference Health Informatics Meets Digital Health - Schönbrunn Palace, Wien, Austria
Duration: 28 May 201929 May 2019
http://www.ehealth2017.at

Publication series

NameStudies in health technology and informatics
Volume260

Conference

Conference13th Annual Conference Health Informatics Meets Digital Health
Abbreviated titledHealth 2019
CountryAustria
CityWien
Period28/05/1929/05/19
Internet address

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Keywords

  • biomarkers
  • classification
  • kinetics
  • time series

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Biomedical Engineering

Fields of Expertise

  • Human- & Biotechnology

Cite this

Netzer, M., Hanser, F., Breit, M., Weinberger, K. M., Baumgartner, C., & Baumgarten, D. (2019). Ensemble Based Approach for Time Series Classification in Metabolomics. In D. Hayn, A. Eggerth, & G. Schreier (Eds.), dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference (pp. 89-96). (Studies in health technology and informatics; Vol. 260). IOS Press. https://doi.org/10.3233/978-1-61499-971-3-89