{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/8068"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/8068","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning classifiers from medical data","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Billing, Jeffrey J. (Jeffrey Joel), 1979-"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Leslie Kaelbling."],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002","date_published":"2002","updated_at":"2026-07-22T22:21:30Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. 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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."],"dc:description.degree":["M.Eng.and S.B."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/8068"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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. 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