A Differential Evolution-Based System Supporting Medical Diagnosis through Automatic Knowledge Extraction from Databases

In this paper, a new approach based on Differential Evolution for the automatic classification of items in medical databases is proposed. Based on it, a tool called DEREx is presented, which automatically extracts explicit knowledge from the database under the form of IF-THEN rules. DEREx is thought as a useful support to decision making whenever explanations on why an item is assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The tool has been compared over seven medical databases against a set of fifteen classification tools widely used in literature. The results have proven the effectiveness of the proposed approach, since DEREx turns out to be among the very best tools in terms of highest classification accuracy, so it is preferable because it automatically extracts knowledge and provides users with it under an easily comprehensible form.