Ensemble Based Approach for Time Series Classification in Metabolomics

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

doi:10.3233/978-1-61499-971-3-89

Ensemble Based Approach for Time Series Classification in Metabolomics

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

Institute of Health Care Engineering with European Testing Center of Medical Devices (7180)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-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 language	English
Title of host publication	dHealth 2019 - From eHealth to dHelath
Subtitle of host publication	Proceedings of the 13th Health Informatics Meets Digital Health Conference
Editors	Dieter Hayn, Alphons Eggerth, Günter Schreier
Publisher	IOS Press
Pages	89-96
Number of pages	8
ISBN (Print)	978-1-61499-974-4
DOIs	https://doi.org/10.3233/978-1-61499-971-3-89
Publication status	Published - 2019
Event	13th Annual Conference Health Informatics Meets Digital Health - Schönbrunn Palace, Wien, Austria Duration: 28 May 2019 → 29 May 2019 http://www.ehealth2017.at

Publication series

Name	Studies in health technology and informatics
Volume	260

Conference

Conference	13th Annual Conference Health Informatics Meets Digital Health
Abbreviated title	dHealth 2019
Country/Territory	Austria
City	Wien
Period	28/05/19 → 29/05/19
Internet address	http://www.ehealth2017.at

Keywords

biomarkers
classification
kinetics
time series

ASJC Scopus subject areas

Health Information Management
Health Informatics
Biomedical Engineering

Fields of Expertise

Human- & Biotechnology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/978-1-61499-971-3-89Licence: CC BY-NC 4.0

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

Ensemble Based Approach for Time Series Classification in Metabolomics. / Netzer, Michael; Hanser, Friedrich; Breit, Marc et al.
dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference. ed. / Dieter Hayn; Alphons Eggerth; Günter Schreier. IOS Press, 2019. p. 89-96 (Studies in health technology and informatics; Vol. 260).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Netzer, M, Hanser, F, Breit, M, Weinberger, KM, 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. Studies in health technology and informatics, vol. 260, IOS Press, pp. 89-96, 13th Annual Conference Health Informatics Meets Digital Health , Wien, Austria, 28/05/19. https://doi.org/10.3233/978-1-61499-971-3-89

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

Netzer, Michael ; Hanser, Friedrich ; Breit, Marc et al. / Ensemble Based Approach for Time Series Classification in Metabolomics. dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference. editor / Dieter Hayn ; Alphons Eggerth ; Günter Schreier. IOS Press, 2019. pp. 89-96 (Studies in health technology and informatics).

@inproceedings{979b6eb036bc41a4857be40cd1f9dd4b,

title = "Ensemble Based Approach for Time Series Classification in Metabolomics",

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 na{\"i}ve 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.",

keywords = "biomarkers, classification, kinetics, time series",

author = "Michael Netzer and Friedrich Hanser and Marc Breit and K.M. Weinberger and Christian Baumgartner and Daniel Baumgarten",

year = "2019",

doi = "10.3233/978-1-61499-971-3-89",

language = "English",

isbn = "978-1-61499-974-4",

series = "Studies in health technology and informatics",

publisher = "IOS Press",

pages = "89--96",

editor = "Dieter Hayn and Alphons Eggerth and G{\"u}nter Schreier",

booktitle = "dHealth 2019 - From eHealth to dHelath",

address = "Netherlands",

note = "13th Annual Conference Health Informatics Meets Digital Health , dHealth 2019 ; Conference date: 28-05-2019 Through 29-05-2019",

url = "http://www.ehealth2017.at",

}

TY - GEN

T1 - Ensemble Based Approach for Time Series Classification in Metabolomics

AU - Netzer, Michael

AU - Hanser, Friedrich

AU - Breit, Marc

AU - Weinberger, K.M.

AU - Baumgartner, Christian

AU - Baumgarten, Daniel

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - biomarkers

KW - classification

KW - kinetics

KW - time series

UR - https://graz.pure.elsevier.com/en/publications/ensemble-based-approach-for-time-series-classification-in-metabolomics(979b6eb0-36bc-41a4-857b-e40cd1f9dd4b).html

UR - http://www.scopus.com/inward/record.url?scp=85066474556&partnerID=8YFLogxK

U2 - 10.3233/978-1-61499-971-3-89

DO - 10.3233/978-1-61499-971-3-89

M3 - Conference paper

SN - 978-1-61499-974-4

T3 - Studies in health technology and informatics

SP - 89

EP - 96

BT - dHealth 2019 - From eHealth to dHelath

A2 - Hayn, Dieter

A2 - Eggerth, Alphons

A2 - Schreier, Günter

PB - IOS Press

T2 - 13th Annual Conference Health Informatics Meets Digital Health

Y2 - 28 May 2019 through 29 May 2019

ER -

Ensemble Based Approach for Time Series Classification in Metabolomics

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this