Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2011
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/64601 |
| _version_ | 1826198186338811904 |
|---|---|
| author | Singh, Anima, Ph. D. Massachusetts Institute of Technology |
| author2 | John V. Guttag. |
| author_facet | John V. Guttag. Singh, Anima, Ph. D. Massachusetts Institute of Technology |
| author_sort | Singh, Anima, Ph. D. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T11:00:58Z |
| format | Thesis |
| id | mit-1721.1/64601 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:00:58Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/646012019-04-12T13:25:35Z Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures Singh, Anima, Ph. D. Massachusetts Institute of Technology John V. Guttag. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2011-06-20T15:58:29Z 2011-06-20T15:58:29Z 2011 2011 Thesis http://hdl.handle.net/1721.1/64601 727068602 eng 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 100 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Singh, Anima, Ph. D. Massachusetts Institute of Technology Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures |
| title | Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures |
| title_full | Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures |
| title_fullStr | Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures |
| title_full_unstemmed | Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures |
| title_short | Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures |
| title_sort | risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non symmetric entropy measures |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/64601 |
| work_keys_str_mv | AT singhanimaphdmassachusettsinstituteoftechnology riskstratificationofcardiovascularpatientsusinganovelclassificationtreeinductionalgorithmwithnonsymmetricentropymeasures |