A Medical Diagnosis Support System based on Automatic Knowledge Extraction from Databases through Differential Evolution

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 fulfill this task is based on Differential Evolution 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 fteen well-known classication tools. Numerical results in terms of classication accuracy, and their statistical analysis, have evidenced the eectiveness 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.