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Regression, Classification and Ensemble Machine Learning Approaches to Forecasting Clinical Outcomes in Ischemic Stroke

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Book cover Biomedical Engineering Systems and Technologies (BIOSTEC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 881))

Abstract

We applied different machine learning approaches to predict (forecast) the clinical outcome, measured by the modified Rankin Scale (mRS) score, of ischemic stroke patients 90 days after stroke. Regression, multinomial classification, and ordinal regression tasks were considered. M5 model trees followed by bootstrap aggregating as a meta-learning technique produced the best regression results. The same regression technique when used for classification after discretization of the target attribute also performed better than regular multinomial classification. For the ordinal regression task, the logit link function (ordinal logistic regression) outperformed the alternatives. We discuss the methodology used, and compare the results with other standard predictive techniques. We also analyze the results to provide insights into the factors that affect stroke outcomes.

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Acknowledgements

The authors thank Prof. Dr. Klaus Brinker for suggesting using ordinal regression as an additional technique in this research.

Author information

Authors and Affiliations

  1. Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA

    Ahmedul Kabir & Carolina Ruiz

  2. Boston College, Chestnut Hill, MA, 02467, USA

    Sergio A. Alvarez

  3. University Massachusetts Medical School, Worcester, MA, 01655, USA

    Majaz Moonis

Authors
  1. Ahmedul Kabir
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  2. Carolina Ruiz
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  3. Sergio A. Alvarez
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  4. Majaz Moonis
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Corresponding author

Correspondence to Carolina Ruiz .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Nathalia Peixoto

  2. Instituto Superior Técnico (IST), University of Lisbon, Lisbon, Portugal

    Margarida Silveira

  3. University of Nebraska at Omaha, Omaha, Nebraska, USA

    Hesham H. Ali

  4. University of Sao Paulo, Sao Carlos, SP, Brazil

    Carlos Maciel

  5. Utrecht University, Utrecht, The Netherlands

    Egon L. van den Broek

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Kabir, A., Ruiz, C., Alvarez, S.A., Moonis, M. (2018). Regression, Classification and Ensemble Machine Learning Approaches to Forecasting Clinical Outcomes in Ischemic Stroke. In: Peixoto, N., Silveira, M., Ali, H., Maciel, C., van den Broek, E. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2017. Communications in Computer and Information Science, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-319-94806-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-94806-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94805-8

  • Online ISBN: 978-3-319-94806-5

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