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Authors: Ahmedul Kabir 1 ; Carolina Ruiz 1 ; Sergio A. Alvarez 2 and Majaz Moonis 3

Affiliations: 1 Worcester Polytechnic Institute, United States ; 2 Boston College, United States ; 3 Univ. Massachusetts Medical School, United States

Keyword(s): Ischemic Stroke, mRS Score, M5 Model Tree, Bootstrap Aggregating, Predicting Stroke Outcome.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Development of Assistive Technology ; Health Information Systems ; Software Systems in Medicine ; Therapeutic Systems and Technologies

Abstract: The objective of our study is to predict the clinical outcome of ischemic stroke patients after 90 days of stroke using the modified Rankin Scale (mRS) score. After experimentation with various regression techniques, we discovered that using M5 model trees to predict the score and then using bootstrap aggregating as a meta-learning technique produces the best prediction results. The same regression when followed by classification also performs better than regular multi-class classification. In this paper, we present the methodology used, and compare the results with other standard predictive techniques. We also analyze the results to provide insights on the factors that affect stroke outcomes.

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Paper citation in several formats:
Kabir, A.; Ruiz, C.; Alvarez, S. and Moonis, M. (2017). Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF; ISBN 978-989-758-213-4; ISSN 2184-4305, SciTePress, pages 178-187. DOI: 10.5220/0006282001780187

@conference{healthinf17,
author={Ahmedul Kabir. and Carolina Ruiz. and Sergio A. Alvarez. and Majaz Moonis.},
title={Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF},
year={2017},
pages={178-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006282001780187},
isbn={978-989-758-213-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF
TI - Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees
SN - 978-989-758-213-4
IS - 2184-4305
AU - Kabir, A.
AU - Ruiz, C.
AU - Alvarez, S.
AU - Moonis, M.
PY - 2017
SP - 178
EP - 187
DO - 10.5220/0006282001780187
PB - SciTePress

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