Title: A medical diagnosis support system based on automatic knowledge extraction from databases through differential evolution

Authors: Ivanoe De Falco

Addresses: Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy

Abstract: An intelligent system for supporting medical diagnosis is presented in this paper. The system automatically extracts knowledge from databases as sets of IF-THEN rules. The approach chosen to fulfil this task is based on the differential evolution (DE) algorithm and its implementation results in a tool called DEREx. This tool is aimed at supporting clinicians in their decision making in the diagnostic process, by providing them with clear explanations on the reasons why each item is assigned to a given class. Performance of the tool has been evaluated over seven medical databases and compared against that of fifteen well-known classification tools. Numerical results in terms of classification accuracy and their statistical analysis, have evidenced the effectiveness of the proposed approach, so DEREx is preferable because of its added value, i.e. the knowledge extracted automatically and provided to users in an easily comprehensible form.

Keywords: decision support systems; medical DSS; medical diagnosis; classification; automatic rule extraction; differential evolution; knowledge extraction; intelligent DSS; medical databases; bioinformatics.

DOI: 10.1504/IJDMB.2013.056644

International Journal of Data Mining and Bioinformatics, 2013 Vol.8 No.4, pp.396 - 412

Received: 18 Feb 2012
Accepted: 02 Mar 2012
Published online: 20 Oct 2014 *

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