Genetic programming for knowledge discovery in chest pain diagnosis

Bojarczuk, Celia C. and Lopes, Heitor S. and Freitas, Alex A. (2000) Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Engineering in Medicine and Biology Magazine, 19 (4). pp. 38-44. ISSN 0739-5175. (Full text available)

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This work aims at discovering classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules we have used genetic programming as well as some concepts of data mining, with emphasis on the discovery of comprehensible knowledge. The fitness function used combines a measure of rule comprehensibility with two usual indicators in medical domain: sensitivity and specificity. Results regarding the predictive accuracy of the discovered rule set as a whole and the predictive accuracy of individual rules are presented and compared to other approaches.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 09 Sep 2009 13:01 UTC
Last Modified: 20 May 2014 08:20 UTC
Resource URI: (The current URI for this page, for reference purposes)
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