2026-09-18T23:41:45Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/646012022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
John V. Guttag.
Singh, Anima, Ph. D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-06-20T15:58:29Z
2011-06-20T15:58:29Z
2011
2011
http://hdl.handle.net/1721.1/64601
727068602
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 95-100).
Risk stratification allows clinicians to choose treatments consistent with a patient's risk profile. Risk stratification models that integrate information from several risk attributes can aid clinical decision making. One of the technical challenges in developing risk stratification models from medical data is the class imbalance problem. Typically the number of patients that experience a serious medical event is a small subset of the entire population. The goal of my thesis work is to develop automated tools to build risk stratification models that can handle unbalanced datasets and improve risk stratification. We propose a novel classification tree induction algorithm that uses non-symmetric entropy measures to construct classification trees. We apply our methods to the application of identifying patients at high risk of cardiovascular mortality. We tested our approach on a set of 4200 patients who had recently suffered from a non-ST-elevation acute coronary syndrome. When compared to classification tree models generated using other measures proposed in the literature, the tree models constructed using non-symmetric entropy had higher recall and precision. Our models significantly outperformed models generated using logistic regression - a standard method of developing multivariate risk stratification models in the literature.
by Anima Singh.
S.M.
100 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="54814c63-cb92-4862-812d-ce7623d27a39">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2011</PublicationDate>
<Authors>
<Author>
<DisplayName>Singh, Anima, Ph. D. Massachusetts Institute of Technology</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Risk stratification allows clinicians to choose treatments consistent with a patient's risk profile. Risk stratification models that integrate information from several risk attributes can aid clinical decision making. One of the technical challenges in developing risk stratification models from medical data is the class imbalance problem. Typically the number of patients that experience a serious medical event is a small subset of the entire population. The goal of my thesis work is to develop automated tools to build risk stratification models that can handle unbalanced datasets and improve risk stratification. We propose a novel classification tree induction algorithm that uses non-symmetric entropy measures to construct classification trees. We apply our methods to the application of identifying patients at high risk of cardiovascular mortality. We tested our approach on a set of 4200 patients who had recently suffered from a non-ST-elevation acute coronary syndrome. When compared to classification tree models generated using other measures proposed in the literature, the tree models constructed using non-symmetric entropy had higher recall and precision. Our models significantly outperformed models generated using logistic regression - a standard method of developing multivariate risk stratification models in the literature.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>