Model-based Clustering of Ischemic Stroke Patients

Ahmedul Kabir, Carolina Ruiz, Sergio Alvarez, Nazish Riaz, Majaz Moonis

Abstract

The objective of our study is to find meaningful groups in the data of ischemic stroke patients using unsupervised clustering. The data are modeled using Gaussian mixture models with a variety of covariance structures. Cluster parameters in each of these models are estimated by maximum likelihood via the Expectation-Maximization algorithm. The best models are then selected by relying on information-theoretic criteria. It is observed that the stroke patients can be grouped into a small number of medically relevant clusters that are defined primarily by the presence of diabetes and atrial fibrillation. Characteristics of the clusters found are discussed, using statistical comparisons and data visualization.

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Paper Citation


in Harvard Style

Kabir A., Ruiz C., Alvarez S., Riaz N. and Moonis M. (2015). Model-based Clustering of Ischemic Stroke Patients . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 172-181. DOI: 10.5220/0005278101720181


in Bibtex Style

@conference{healthinf15,
author={Ahmedul Kabir and Carolina Ruiz and Sergio Alvarez and Nazish Riaz and Majaz Moonis},
title={Model-based Clustering of Ischemic Stroke Patients},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={172-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005278101720181},
isbn={978-989-758-068-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Model-based Clustering of Ischemic Stroke Patients
SN - 978-989-758-068-0
AU - Kabir A.
AU - Ruiz C.
AU - Alvarez S.
AU - Riaz N.
AU - Moonis M.
PY - 2015
SP - 172
EP - 181
DO - 10.5220/0005278101720181