Learning classifiers from medical data

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2002

Author dc:creator

• Billing, Jeffrey J. (Jeffrey Joel), 1979-

Advisor dc:contributor.advisor

• Leslie Kaelbling.

• Electrical Engineering and Computer Science.

Statement 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. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/8068

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8068