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Genetic programming for knowledge discovery in chest pain diagnosis

Bojarczuk, Celia C., Lopes, Heitor S., 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. (KAR id:22004)

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Abstract

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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 09 Sep 2009 13:01 UTC
Last Modified: 16 Feb 2021 12:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/22004 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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