Learning classifiers from medical data
Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/8068 |
| _version_ | 1811081341763059712 |
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| author | Billing, Jeffrey J. (Jeffrey Joel), 1979- |
| author2 | Leslie Kaelbling. |
| author_facet | Leslie Kaelbling. Billing, Jeffrey J. (Jeffrey Joel), 1979- |
| author_sort | Billing, Jeffrey J. (Jeffrey Joel), 1979- |
| collection | MIT |
| description | Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. |
| first_indexed | 2024-09-23T11:45:14Z |
| format | Thesis |
| id | mit-1721.1/8068 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:45:14Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/80682019-04-11T12:57:41Z Learning classifiers from medical data Learning Bayesian networks from medical data Billing, Jeffrey J. (Jeffrey Joel), 1979- Leslie Kaelbling. 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 (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. Includes bibliographical references (leaf 32). The goal of this thesis was to use machine-learning techniques to discover classifiers from a database of medical data. Through the use of two software programs, C5.0 and SVMLight, we analyzed a database of 150 patients who had been operated on by Dr. David Rattner of the Massachusetts General Hospital. C5.0 is an algorithm that learns decision trees from data while SVMLight learns support vector machines from the data. With both techniques we performed cross-validation analysis and both failed to produce acceptable error rates. The end result of the research was that no classifiers could be found which performed well upon cross-validation analysis. Nonetheless, this paper provides a thorough examination of the different issues that arise during the analysis of medical data as well as describes the different techniques that were used as well as the different issues with the data that affected the performance of these techniques. by Jeffrey J. Billing. M.Eng.and S.B. 2005-08-24T20:04:55Z 2005-08-24T20:04:55Z 2002 2002 Thesis http://hdl.handle.net/1721.1/8068 51110975 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 46 leaves 4092512 bytes 4092269 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Billing, Jeffrey J. (Jeffrey Joel), 1979- Learning classifiers from medical data |
| title | Learning classifiers from medical data |
| title_full | Learning classifiers from medical data |
| title_fullStr | Learning classifiers from medical data |
| title_full_unstemmed | Learning classifiers from medical data |
| title_short | Learning classifiers from medical data |
| title_sort | learning classifiers from medical data |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/8068 |
| work_keys_str_mv | AT billingjeffreyjjeffreyjoel1979 learningclassifiersfrommedicaldata AT billingjeffreyjjeffreyjoel1979 learningbayesiannetworksfrommedicaldata |